Hydrogen fuel cell vehicle group-oriented life budget constraint VGI cooperative scheduling method and system

Abstract

The invention discloses a hydrogen fuel cell vehicle group-oriented life budget constraint VGI cooperative scheduling method and system. The method comprises the steps of obtaining a capability message sequence of each vehicle; acquiring a power service request sequence from a power grid or a micro-grid; establishing a life budget constraint and an electric pile equivalent ripple constraint; according to the power service request sequence and the capability message sequence, based on life budget constraint, galvanic pile equivalent ripple constraint, galvanic pile ramp rate constraint, ground energy storage power and energy constraint and galvanic pile power operation window constraint, solving by taking minimization of a preset target function as a target to obtain ground energy storage power, galvanic pile power and local energy storage power; each vehicle adjusts the corresponding actual output power according to the electric pile power and the local energy storage power, and the ground energy storage system of the microgrid adjusts the corresponding actual output power according to the ground energy storage power so as to realize VGI cooperative scheduling. According to the invention, when the hydrogen fuel cell vehicle group participates in the VGI service, the service life of the electric pile is prolonged, and the whole life cycle cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of energy management and vehicle-to-grid interaction for hydrogen fuel cell vehicles, and particularly to a collaborative scheduling method for hydrogen fuel cell vehicle fleets that considers stack lifetime constraints. Specifically, it is a lifetime budget constraint VGI collaborative scheduling method and system for hydrogen fuel cell vehicle fleets. Background Technology

[0002] With the rapid development of new energy vehicles, hydrogen fuel cell vehicles have become an important technological path for low-carbon transformation in the transportation sector due to their advantages such as zero emissions, long driving range, and short refueling time. VGI (Vehicle-Grid Integration) enables vehicles to not only be energy consumers but also to participate in grid ancillary services as mobile energy storage units, providing support for the grid in peak shaving, frequency regulation, and demand response.

[0003] However, the proton exchange membrane fuel cell stack, a core component of hydrogen fuel cell systems, suffers from significant lifespan degradation. Stack lifespan is significantly affected by operating conditions; frequent power fluctuations, rapid load changes, and start-stop cycles all accelerate stack performance degradation, leading to premature stack failure and increasing the vehicle's total lifespan cost.

[0004] Existing vehicle-grid interaction scheduling methods primarily focus on meeting power demand and maximizing economic benefits, typically assuming that the vehicle's power system can respond to grid demands without limitation. These methods neglect the lifespan constraints of the hydrogen fuel cell stack, which may lead to rapid stack degradation under frequent power fluctuations, significantly shortening stack lifespan, increasing replacement costs, and reducing the overall economic efficiency of the system.

[0005] Furthermore, existing methods lack consideration for dynamic constraints such as fuel cell stack ripple power and ramp rate, failing to effectively protect fuel cell stack health while meeting grid service demands. For vehicle fleet scenarios, there is a lack of collaborative optimization methods that consider individual vehicle differences, including but not limited to fuel cell stack health status, remaining lifetime budget, and hydrogen quantity. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a life-budget-constrained VGI collaborative scheduling method and system for hydrogen fuel cell vehicle fleets. This method enables hydrogen fuel cell vehicle fleets to participate in VGI services while extending the lifespan of the fuel cell stack, reducing the total life-cycle cost, and improving the system's economic efficiency and sustainability.

[0007] The present invention adopts the following technical solution.

[0008] The first aspect of the present invention provides a lifetime budget-constrained VGI cooperative scheduling method for hydrogen fuel cell vehicle fleets, comprising: Obtain the capability message sequence of each vehicle; Obtain the sequence of power service requests from the external power grid or microgrid; Establish lifetime budget constraints and fuel cell stack equivalent ripple constraints; Based on the power service request sequence and the capacity message sequence, the solution is performed with the objective of minimizing the preset objective function, based on lifetime budget constraints, equivalent ripple constraints of the fuel cell stack, fuel cell stack ramp rate constraints, ground energy storage power and energy constraints, and fuel cell stack power operation window constraints, to obtain the ground energy storage power, fuel cell stack power, and local energy storage power. The corresponding fuel cell stack power and local energy storage power are distributed to each vehicle, and the ground energy storage power is distributed to the ground energy storage system of the microgrid, so that each vehicle and the ground energy storage system can adjust the corresponding actual output power to achieve VGI coordinated scheduling.

[0009] Optionally, the capability message sequence may include at least one of the following: stack health status parameters, remaining lifetime budget, allowable stack power ramp-up limit, allowable stack equivalent ripple limit, remaining hydrogen quantity, minimum stack power in the stack allowable operating power window, and maximum stack power in the stack allowable operating power window.

[0010] Optionally, the power service request sequence includes vehicle fuel cell stack power, ground-based energy storage power, and vehicle-local energy storage power.

[0011] Optionally, the preset objective function is expressed as follows:

[0012] in, Describe the objective function. This represents the ground-based energy storage power at time k. This represents the output power of the fuel cell stack in the i-th vehicle at time k. This represents the charging and discharging power of the local energy storage of the i-th vehicle at time k. This represents the power service request of the power grid at time k, where M represents the total number of vehicles, N represents the prediction time domain length, and k represents the sampling time index.

[0013] Optionally, lifetime budget constraints can be established using the following formula:

[0014] in, This represents the increase in budget consumption for the i-th vehicle. Let N represent the remaining lifetime budget for vehicle i at time k, and let N represent the prediction time domain length.

[0015] Optionally, the equivalent ripple constraint of the fuel cell stack can be established using the following formula:

[0016]

[0017] in, This represents the average output power of the fuel cell stack in the i-th vehicle within the sliding window. This represents the equivalent ripple of the fuel cell stack in vehicle i at time k. This indicates the upper limit of the allowable equivalent ripple of the fuel cell stack. This represents the output power of the fuel cell stack in the i-th vehicle at time j. This indicates the size of the sliding window.

[0018] Optionally, the budgeted consumption increment can be calculated by combining the actual output power of the fuel cell stack, vehicle start-stop indication, temperature overrun indication, and power fluctuation, and the remaining lifetime budget can be updated according to the budgeted consumption increment.

[0019] Optionally, when the preset objective function has no solution within a given prediction time domain, at least one of the following is introduced as a slack variable: the change in stack output power, the minimum stack power in the stack allowable operating power window, the maximum stack power in the stack allowable operating power window, and the power service request sequence difference. The slack variables are added to the preset objective function according to the preset weights to form a new objective function.

[0020] Optionally, the method further includes: When the microgrid is detected to be disconnected from the external power grid or the external power grid is lost, each vehicle is evaluated based on the remaining lifetime budget, the equivalent ripple of the fuel cell stack, the remaining hydrogen quantity, the communication quality between vehicles, and the state of charge of the on-board battery to obtain the total score of each vehicle. The vehicle with the highest total score is selected as the master vehicle, and the remaining vehicles are slave vehicles. The master vehicle broadcasts the range of network parameters corresponding to the fuel cell ramp rate constraint, fuel cell equivalent ripple constraint, lifetime budget constraint, and fuel cell power operating window constraint to the slave vehicle. After the slave vehicle verifies that the above constraints are met locally, it enters parallel operation to form an autonomous microgrid. When the communication quality between the master vehicle and the slave vehicle is lower than the communication quality threshold, the ground energy storage system controls the maintenance of the bus voltage and frequency stability of the autonomous microgrid, and the vehicle side only implements the power regulation strategy under the constraint of the stack ramp rate.

[0021] Optionally, each vehicle can be evaluated based on its remaining lifetime budget, equivalent stack ripple, remaining hydrogen supply, inter-vehicle communication quality, and onboard battery state of charge to obtain a total score for each vehicle, including:

[0022] in, This represents the communication quality score for the i-th vehicle. This represents the normalized remaining lifetime budget for the i-th vehicle at time k. This represents the normalized amount of remaining hydrogen. Let represent the normalized equivalent ripple of the i-th vehicle's fuel cell stack at time k. Let be the state of charge of the onboard battery of vehicle i at time k. , , , , These are the weights for each corresponding scoring indicator.

[0023] A second aspect of the present invention provides a lifetime budget-constrained VGI collaborative scheduling system for hydrogen fuel cell vehicle fleets, implementing the aforementioned lifetime budget-constrained VGI collaborative scheduling method for hydrogen fuel cell vehicle fleets, the system comprising: The first acquisition module is used to acquire the capability message sequence of each vehicle; The second acquisition module is used to acquire power service request sequences from external power grids or microgrids; Establish a module to establish lifetime budget constraints and fuel cell stack equivalent ripple constraints; The solution module is used to solve the power service request sequence and capacity message sequence based on lifetime budget constraints, stack equivalent ripple constraints, stack ramp rate constraints, ground energy storage power and energy constraints, and stack power operation window constraints, with the goal of minimizing a preset objective function, to obtain ground energy storage power, stack power, and local energy storage power. The scheduling module is used to distribute the corresponding fuel cell stack power and local energy storage power to each vehicle, and to distribute the ground energy storage power to the ground energy storage system of the microgrid, so that each vehicle and the ground energy storage system can adjust the corresponding actual output power to achieve VGI coordinated scheduling.

[0024] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when loaded onto the processor, implements the above-described lifetime budget constraint VGI cooperative scheduling method for hydrogen fuel cell vehicle fleets.

[0025] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described lifetime budget constraint VGI cooperative scheduling method for hydrogen fuel cell vehicle fleets.

[0026] Compared with the prior art, the beneficial effects of the present invention include at least the following: This invention provides a lifetime budget-constrained VGI collaborative scheduling method and system for hydrogen fuel cell vehicle fleets. When the vehicle fleet participates in grid ancillary services, it effectively limits stack lifetime damage and avoids premature stack decay. Under the premise of satisfying power service requests, it projects unexecutable power sequences into executable sequences that satisfy lifetime budget, ripple constraints, and ramp constraints. It enables a vehicle fleet aggregator to collaboratively optimize the scheduling of multiple hydrogen fuel cell vehicles and ground-based energy storage systems, considering individual differences in vehicle health status, remaining lifetime budget, and hydrogen quantity. A closed-loop update mechanism ensures accurate tracking and dynamic adjustment of the lifetime budget. It also guarantees the system's self-healing capability and operational reliability in emergency scenarios such as communication failures or grid outages.

[0027] This invention effectively limits the dynamic stress experienced by the fuel cell stack during VGI service by introducing lifetime budget constraints, ripple constraints, and ramp rate constraints, thereby reducing the stack replacement frequency and total lifecycle cost. By extending stack life, reducing replacement costs, and synergistically optimizing the use of ground-based and on-board energy storage, this invention significantly improves the economic feasibility of hydrogen fuel cell vehicle fleets participating in VGI service.

[0028] This invention utilizes an emergency self-healing mode and a master-negotiation-degradation protocol, enabling the system to maintain basic operation and avoid system crashes even in abnormal scenarios such as communication failures and power grid outages. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating a lifetime budget constraint VGI collaborative scheduling method for hydrogen fuel cell vehicle fleets provided in an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the impact of lifetime constraints on power tracking, provided by an embodiment of the present invention. Figure 3 This is a schematic diagram of the slope rate distribution provided in an embodiment of the present invention; Figure 4 This is a statistical diagram of power ripple distribution provided in an embodiment of the present invention; Figure 5 This is a comparison chart of the cumulative lifetime consumption of a fuel cell stack provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of power request and coordinated response provided in an embodiment of the present invention; Figure 7This is a schematic diagram of the output of a vehicle 1 fuel cell stack provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the output of a vehicle 2 fuel cell stack provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of a vehicle 3-fuel cell stack output provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of a vehicle with four fuel cell stacks provided in an embodiment of the present invention; Figure 11 This is a schematic diagram illustrating the changes in the remaining life budget of each vehicle provided in an embodiment of the present invention; Figure 12 This is a schematic diagram of the change in bottom surface energy storage SOC provided in an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0031] An embodiment of the present invention includes a vehicle aggregator, a traffic service area microgrid containing a ground-based energy storage system, and at least one hydrogen fuel cell vehicle. The vehicle aggregator is responsible for receiving vehicle capability messages, obtaining service requests from the power grid and / or the traffic service area microgrid, solving optimization problems, issuing power commands, and updating lifetime budgets. The traffic service area microgrid is used to absorb rapid power changes, smooth ripples, and provide voltage and / or frequency support in emergency self-healing mode. The hydrogen fuel cell vehicle reports capability parameters through VGI communication, executes the issued power commands, and feeds back the actual output.

[0032] Combination Figure 1 As shown, Embodiment 1 of the present invention provides a lifetime budget-constrained VGI cooperative scheduling method for hydrogen fuel cell vehicle fleets, the method comprising the following steps: Step 1: Obtain the capability message sequence for each vehicle.

[0033] The capability message must include at least: the stack health status parameter (SOH). i Remaining life budget Allowable power ramp-up limit for fuel cell stacks Allowable upper limit of equivalent ripple in fuel cell stack Remaining hydrogen content Minimum stack power within the permissible operating power window of the fuel cell stack Maximum stack power within the allowed operating power window .

[0034] Specifically, the i-th vehicle can actively send a capability message to the vehicle group aggregator via VGI, the vehicle group aggregator can obtain the vehicle's capability message, or the microgrid can obtain the vehicle's capability message.

[0035] Step 2: Obtain the power service request sequence from the external power grid or microgrid.

[0036] The power service request sequence includes vehicle fuel cell stack power, ground energy storage power, and vehicle local energy storage power.

[0037] In some embodiments, the vehicle aggregator acquires a sequence of power service requests from the power grid or a traffic service area microgrid, and in some embodiments, the power service request sequence is acquired by the vehicles.

[0038] Step 3: Establish lifetime budget constraints and stack equivalent ripple constraints. Based on the power service request and capacity message sequence, solve the problem by minimizing the preset objective function, based on lifetime budget constraints, stack equivalent ripple constraints, stack ramp rate constraints, ground energy storage power and energy constraints, and stack power operation window constraints, to obtain the ground energy storage power, the stack power of each vehicle, and the local energy storage power of each vehicle.

[0039] Specifically, the vehicle aggregator uses the prediction time domain length N as a rolling window to solve the optimization problem and obtain the ground energy storage power, the power of each vehicle's fuel cell stack, and the local energy storage power of each vehicle, so that the power service request is projected into an executable sequence that satisfies the lifetime budget and ripple constraints.

[0040] The preset objective function is expressed as follows:

[0041] in, Let N represent the objective function, N represent the prediction time domain length, and k represent the sampling time index. This represents the ground-based energy storage power at time k. This represents the output power of the fuel cell stack in the i-th vehicle at time k. This represents the charging and discharging power of the local energy storage of the i-th vehicle at time k. This represents the power service request of the power grid at time k, where M represents the total number of vehicles.

[0042] The objective function aims to minimize the sum of squares of the deviations between the actual total output power and the requested power, thereby achieving high-precision tracking of service requests. The stack ramp rate constraint is expressed by the following formula:

[0043] in, This indicates the maximum allowable power ramp-up limit for the fuel cell stack. Indicates the sampling period. This represents the output power of the fuel cell stack in the i-th vehicle at time k-1. This represents the change in the output power of the fuel cell stack of the i-th vehicle at adjacent time points.

[0044] This application avoids mechanical stress and localized overheating of the MEA (Membrane Electrode Assembly) caused by rapid load changes by limiting the unit time change of the stack power, thereby delaying performance degradation.

[0045] The following equivalent ripple constraint for the fuel cell stack is defined within a sliding window of size W:

[0046]

[0047] in, This represents the moving average power within the sliding window. This represents the equivalent ripple of the fuel cell stack in vehicle i at time k. This indicates the upper limit of the allowable equivalent ripple of the fuel cell stack. This represents the output power of the fuel cell stack in the i-th vehicle at time j. This indicates the size of the sliding window.

[0048] In this embodiment, the fluctuation range of the stack power around its moving average is limited by equivalent stack ripple constraint. High-frequency power fluctuations can lead to repeated oxidation-reduction of the catalyst layer and periodic changes in membrane water content, accelerating stack degradation. By limiting ripple, the stack is ensured to operate under relatively stable conditions.

[0049] Lifetime budget constraints are expressed as follows:

[0050] in, This represents the increase in budget consumption for the i-th vehicle. Let $\frac{i}{k}$ represent the remaining lifespan budget for vehicle $i$ at time $k$.

[0051] Lifetime budget constraints ensure that the cumulative lifetime consumption within the forecast time domain does not exceed the vehicle's current remaining lifetime budget.

[0052] Specifically, lifespan budget The settings can be configured by the vehicle owner based on factors such as the actual health status of the fuel cell stack, planned service life, and replacement costs, and include the following steps: First, perform initialization:

[0053] in, Let's estimate the remaining lifespan of the i-th vehicle at time 0. The parameters represent the actual health status of the fuel cell stack at time 0 for the i-th vehicle. This is the end-of-life threshold. ∈[0,1] represents the percentage of the fleet's lifespan that can be used for vehicle-to-everything (V2X) services. This value is used to characterize the planned usage period and replacement costs. When replacement costs are high and the usage period is long, a smaller value can be set. , Reference lifetime flux, in kW·h. This refers to the nominal output power of the fuel cell stack under reference operating conditions. This represents the nominal lifespan of the fuel cell stack under reference operating conditions.

[0054] Subsequently, online updates are performed based on budget consumption increments:

[0055] in, This represents the output power of the fuel cell stack in the i-th vehicle at time k. This represents the output power of the fuel cell stack in the i-th vehicle at time k-1. This represents the moving average power within the sliding window. This is the start / stop indication value for the i-th vehicle at time k, which is 1 when starting or stopping, and 0 otherwise; This is the temperature out-of-range indication value for the i-th vehicle at time k. It is 1 when the fuel cell temperature exceeds the safe range, and 0 otherwise. , , and These are non-negative weighting coefficients, calibrated based on experimental data or attenuation models provided by fuel cell stack manufacturers. This budgeted consumption increment model quantifies various physical attenuation mechanisms, including load cycling, power fluctuations, start-stop damage, and thermal stress, into equivalent lifetime consumption, providing a calculable constraint basis for the optimization algorithm.

[0056] Ground-based energy storage and energy constraints are expressed by the following formula:

[0057]

[0058]

[0059]

[0060]

[0061] in, This indicates the maximum ground-based energy storage capacity; This represents the energy stored on the ground at time k+1. This represents the energy stored at time k in ground-based energy storage. This represents the discharge power of the ground-based energy storage at time k. This represents the charging power of ground-based energy storage at time k. This represents the ground-based energy storage power at time k. This indicates the minimum energy allowed for ground-based energy storage. This indicates the maximum energy allowed for ground-based energy storage. , These represent discharge efficiency and charging efficiency, respectively.

[0062] The fuel cell stack power operating window constraint is expressed by the following formula:

[0063] in, This represents the minimum stack power within the allowed operating power window for the i-th vehicle's fuel cell stack. This indicates the maximum stack power within the allowed operating power window of the fuel cell stack.

[0064] In this embodiment, the core output of the coordinated scheduling of the hydrogen fuel cell vehicle fleet participating in vehicle-grid interaction is a sequence of directly executable vehicle stack power and energy storage power scheduling commands, thereby achieving process tracking of power service requests from the grid or microgrid. Furthermore, this embodiment establishes a constraint system around the dynamic stress and lifespan boundaries of the hydrogen fuel cell stack, emphasizing the explicit inclusion of constraints such as lifespan budget, equivalent ripple, and power change rate in the solution. A closed-loop update mechanism continuously corrects lifespan budget consumption, thus better aligning with the engineering feasibility and durability goals of the fuel cell vehicle fleet.

[0065] The vehicle aggregator constructs a set of vehicle feasible regions, which is the set of all vehicles that simultaneously satisfy the fuel cell ramp rate constraint, fuel cell equivalent ripple constraint, lifetime budget constraint, and fuel cell power operating window constraint.

[0066] When the objective function is not feasible within a given scrolling window, slack variables are introduced. Non-lifetime constraints are relaxed in stages, while lifetime budget constraints and stack equivalent ripple constraints remain as non-relaxable hard constraints, and the relaxation cost is added to the objective function:

[0067] in, The objective function after introducing relaxation costs, To pre-determine the weights, slack variables can be introduced for at least one of the following: the change in stack output power, the minimum stack power in the stack's allowed operating power window, the maximum stack power in the stack's allowed operating power window, and the difference between the power service request sequences.

[0068] It is understandable that the change in the stack output power can be the increase or decrease in the stack power at adjacent sampling times. By introducing slack variables, a solution can still be found even when the objective function is infeasible within a given rolling window. This ensures that, under extreme demand scenarios, priority is given to protecting the stack lifetime, while allowing for a moderate relaxation of other constraints to maintain system feasibility.

[0069] This disclosure uses the satisfaction of external power service requests as a driving force to map the demand sequence in the rolling time domain into an executable scheduling sequence that satisfies constraints such as lifetime budget, equivalent ripple, power change rate, power window and ground energy storage. When infeasibility is not possible, it reflects a hierarchical processing strategy for different constraints, thereby prioritizing the protection of lifetime-related boundaries and operational safety.

[0070] This disclosure provides executable output solutions under dynamic operating conditions such as rapid changes and high-frequency fluctuations in grid power service requests. Through rolling optimization involving vehicle-to-everything (V2X) aggregation and collaboration with ground-based energy storage and / or onboard batteries, it improves power point tracking (PPT) performance and engineering availability. Output assessment is aligned with the vehicle's physical feasible domain (pile power limits, gradeability, equivalent ripple, lifetime budget, and onboard battery charging / discharging capacity), reducing the risk of "assessed as schedulable but not actually executable" and causing excessive stress on the pile. By updating system metrics online / rollingly, and by implementing tiered relaxation and penalties for non-lifetime constraints in extreme scenarios, scheduling results can be corrected in real-time based on requests and resource status, improving response timeliness and resource utilization efficiency.

[0071] It should be noted that in some embodiments, the objective function is solved after the constraints are established on the vehicle side, or the objective function is solved after the constraints are established on the microgrid side, or the objective function is solved in the vehicle aggregator after the constraints are established on the vehicle. This invention does not limit this.

[0072] Step 4: Each vehicle adjusts its actual output power according to the power of the fuel cell stack and the local energy storage power. The ground energy storage system of the microgrid adjusts its actual output power according to the ground energy storage power to achieve VGI coordinated scheduling.

[0073] In some embodiments, the vehicle group aggregator distributes the fuel cell power of each vehicle to each vehicle individually. With the local energy storage power of each vehicle And distribute the ground-based energy storage capacity P to the microgrid in the traffic service area. ess [k]. Each execution unit adjusts its actual output power according to the instructions.

[0074] Step 5: Calculate the budgeted consumption increment based on the actual output power of the fuel cell stack, vehicle start-stop indication, temperature overrun indication, and power fluctuation, and update the remaining lifetime budget according to the budgeted consumption increment.

[0075] Specifically, the vehicle records the actual output power of the fuel cell stack and sends it back to the vehicle aggregator. The vehicle aggregator combines the fuel cell stack's output power, vehicle start / stop indications, temperature overrun indications, and power fluctuations to calculate the budgeted consumption increment, and updates the lifespan budget using the following formula before proceeding to the next scrolling window:

[0076] In this embodiment, a closed-loop feedback mechanism is used to ensure accurate tracking of the lifetime budget and prevent excessive fuel cell degradation caused by budget overruns.

[0077] In this embodiment, the fuel cell stack lifetime budget is introduced as an explicit constraint into the vehicle group VGI collaborative scheduling optimization problem. A closed-loop update mechanism ensures accurate tracking and dynamic adjustment of lifetime consumption, significantly extending the fuel cell stack's lifespan while maintaining service quality. A comprehensive optimization framework integrating ramp rate constraints, equivalent ripple constraints, lifetime budget constraints, and ground-based energy storage constraints is constructed to achieve all-round control of the fuel cell stack's dynamic stress. In particular, the innovative design of the equivalent ripple constraint can effectively suppress the damage to the fuel cell stack caused by high-frequency power fluctuations. Considering the individual differences in vehicle health status, remaining lifetime budget, hydrogen quantity, etc., a vehicle-specific power allocation strategy is implemented.

[0078] Step 6: When the microgrid in the traffic service area is detected to be disconnected from the external power grid or the external power grid is found to be out of power, execute the master selection-negotiation-degradation protocol.

[0079] It is understandable that disconnection means that the electrical connection between the traffic service area microgrid and the external main power grid is actively or passively disconnected, thereby causing the traffic service area microgrid to disconnect from the main power grid and form an independently operating island system.

[0080] This invention further includes an emergency self-healing mode. When the service area microgrid is detected to be disconnected from the external power grid or the external power grid loses voltage, each vehicle is evaluated based on the remaining lifetime budget, equivalent ripple of the fuel cell stack, remaining hydrogen quantity, inter-vehicle communication quality, and on-board battery state of charge. A total score is obtained for each vehicle, and the vehicle with the highest total score is selected as the master vehicle, and the remaining vehicles are slave vehicles. The master vehicle broadcasts the network parameter ranges corresponding to the fuel cell stack ramp rate constraint, equivalent ripple of the fuel cell stack, lifetime budget constraint, and fuel cell stack power operation window constraint to the slave vehicles. The slave vehicles verify locally that the above constraints are met. Then, it enters parallel operation to form an autonomous microgrid. When the communication quality between the master vehicle and the slave vehicle is lower than the communication quality threshold, the system automatically enters the degradation mode, which is controlled by the ground energy storage system to maintain the stability of the autonomous microgrid bus voltage and frequency. The vehicle side exits the fine-grained coordinated scheduling based on rolling optimization and only executes the power regulation strategy with limited ramp rate. That is, while maintaining the previous steady-state power, the output is slowly adjusted according to the preset maximum ramp rate, or smooth tracking is performed based on the received low-frequency power setpoint, so as to reduce the dependence on communication and avoid operational oscillation or instability, and ensure basic power supply continuity.

[0081] In this embodiment, by executing the master election-negotiation-degradation protocol, the voltage / frequency stability and critical load power supply of the autonomous microgrid can still be maintained under abnormal scenarios, and the changes in vehicle output can be limited to reduce the dynamic stress of the fuel cell stack and the risk of scheduling failure.

[0082] Step 6 specifically includes: Step 6.1: Normalize the indicators, specifically including:

[0083] in, This is a preset lifespan reference value. This is a preset reference value for hydrogen storage. To preset the equivalent ripple reference value, you can take the fleet's rated value or the current fleet's maximum value. Let be the remaining hydrogen amount in the i-th vehicle. This represents the remaining lifespan budget of the i-th vehicle at time k. Let be the equivalent ripple of the fuel cell stack of vehicle i at time k. The above methods normalize each index to the range [0,1], thus ensuring dimensional consistency.

[0084] Step 6.2: Calculate the comprehensive score for each vehicle based on the normalized remaining lifetime budget index, the equivalent ripple index of the fuel cell stack, and the remaining hydrogen content index.

[0085] in, The communication quality score for the i-th vehicle is given, and the communication quality score ranges from [0,1]. This represents the normalized remaining lifetime budget for the i-th vehicle at time k. This represents the normalized amount of remaining hydrogen. Let represent the normalized equivalent ripple of the i-th vehicle's fuel cell stack at time k. Let be the state of charge of the onboard battery of vehicle i at time k. , , , , These represent the weights of the corresponding scoring indicators. The vehicle with the highest overall score is selected as the primary vehicle.

[0086] In some embodiments, communication quality scoring The packet loss rate between the vehicle and the host vehicle can be used as a metric. calculate:

[0087] Step 6.3: Based on the range of network parameters corresponding to the above constraints broadcast by the master vehicle as determined in Step 6.2, the slave vehicle enters parallel operation only after verifying locally that the fuel cell ramp rate constraint, fuel cell equivalent ripple constraint, lifetime budget constraint, and fuel cell power operating window constraint are met, thus forming an autonomous microgrid.

[0088] It is understandable that parallel operation refers to the parallel operation between vehicles and the parallel operation between vehicles and busbars.

[0089] In this embodiment, multiple vehicles are simultaneously connected to the same microgrid bus to achieve parallel operation. This creates an autonomous microgrid that can independently maintain bus voltage and frequency and continuously supply power without external grid support. In other words, the power is still calculated according to steps 1 to 4, and the power service request of the grid at time k in the objective function is... No longer affected by the external power grid, it can independently maintain bus voltage and frequency and supply power continuously without the support of the external power grid.

[0090] Step 6.4: When the packet loss rate of communication between the main vehicle and other vehicles exceeds the preset threshold, the system automatically degrades to a mode in which the ground energy storage network is formed and the vehicles are only allowed to change their output slowly. The ground energy storage system undertakes the network control to maintain the bus voltage and frequency and suppress rapid power fluctuations. The vehicles are only allowed to change their output slowly to reduce the dependence on high-frequency communication and fine coordination, avoid oscillation or instability of the parallel system, and ensure basic power supply continuity.

[0091] When the packet loss rate in communication between the main vehicle and other vehicles exceeds a preset packet loss rate threshold, the system automatically enters a degradation mode, ceasing the fine-grained coordinated power allocation that relies on high-frequency communication, and switching to an autonomous operation mode controlled by a ground-based energy storage network with vehicles slowly outputting power. Specifically: The power converter of the ground-based energy storage system is switched to voltage source grid control, with the voltage amplitude and frequency of the microgrid bus as the control target. The voltage and frequency reference of the microgrid in the service area is established and maintained, and the system takes priority in handling the rapid power fluctuations of the microgrid net load. When the vehicle side is degraded, the previous steady-state power or the last valid setpoint is frozen as the frozen reference value. In subsequent operation, the output is only allowed to be slowly adjusted within the power window allowed by the fuel cell stack according to the preset maximum ramp rate. If the low-frequency power setpoint can still be received, the low-frequency power setpoint is first smoothed and then gradually tracked under the ramp rate constraint. If the low-frequency power setpoint cannot be received, it is kept near the frozen reference value and only slightly adjusted. Through the above division of labor, the vehicle only undertakes the slow power sharing and the ground-based energy storage undertakes the rapid disturbance suppression, thereby reducing the dependence on communication and avoiding power oscillation or instability of the parallel system, and ensuring basic power supply continuity.

[0092] Combination Figures 2 to 5As shown, the effects of lifetime budget constraints, ramp rate constraints, and ripple constraints on the VGI collaborative scheduling strategy of hydrogen fuel cell vehicle fleet are demonstrated through comparative simulation experiments. The scheduling behavior of 10 hydrogen fuel cell vehicles (each with a rated power of 60kW) during a 2-hour continuous VGI service period is simulated. Figures 2 to 5 The comparative analysis was conducted from four dimensions: power point tracking, ramp rate statistics, ripple statistics, and cumulative lifetime consumption.

[0093] Combination Figures 6 to 12 As shown, Figures 6 to 12 This paper demonstrates the real-time collaborative scheduling process of a hydrogen fuel cell vehicle fleet during a 60-minute VGI service period. It simulates a real-world scenario in which 10 hydrogen fuel cell buses in a highway service area respond to grid peak-shaving requests, verifying the effectiveness of the rolling time-domain optimization algorithm, multi-level collaborative strategy, and lifetime budget closed-loop update mechanism proposed in this invention.

[0094] Embodiment 2 of the present invention provides an application example of a lifetime budget-constrained VGI collaborative scheduling method for hydrogen fuel cell vehicle fleets. Specifically: A highway service area has a hydrogen fuel cell bus parking lot, equipped with 10 hydrogen fuel cell buses. Each bus has a fuel cell stack with a rated power of 60 kW, a ground-based energy storage system with a capacity of 200 kWh, a maximum charge / discharge power of 100 kW, and a renewable energy hydrogen production system. The service area's microgrid is connected to the regional power grid and participates in grid peak shaving ancillary services. The sampling period is Δt = 1 minute, the prediction time domain length is N = 15 minutes, the sliding window length is W = 10 minutes, the remaining lifespan budget for each vehicle is Bi(0) = 500-800 kW·h, and the allowable ramp limit is... =10 kW / min, the upper limit of permissible ripple is =3kW, stack operating window is = 10 kW, = 60 kW. Implementation steps include: Step 1: At each sampling time k (i.e., every minute), each vehicle reports its capabilities to the vehicle group aggregator via VGI communication, including the current... Remaining budget Hydrogen content wait.

[0095] Step 2: The vehicle aggregator obtains the power demand sequence for the next 15 minutes from the power grid dispatch center, for example, requesting the vehicle group to provide 150 kW of continuous output for 15 minutes during peak hours.

[0096] Step 3: The vehicle aggregator solves the optimization problem. Under the premise of satisfying the constraints of stack ramp rate, stack equivalent ripple, lifetime budget, ground energy storage power and capacity, and stack power operation window, the ground energy storage power, the stack power of each vehicle, and the local energy storage power of each vehicle are calculated.

[0097] Step 4: The vehicle aggregator sends the generator power and local energy storage power to each vehicle via VGI, and sends the ground energy storage power to the microgrid controller. Each vehicle and the ground energy storage executes the instructions.

[0098] Step 5: The vehicle reports the actual fuel cell stack output power. The vehicle aggregator calculates the actual budget consumption increment based on the equivalent damage model and updates the remaining budget. The scrolling window advances one step to the next time step k+1 for optimization.

[0099] Embodiment 3 of the present invention provides a lifetime budget-constrained VGI collaborative scheduling system for hydrogen fuel cell vehicle fleets, implementing the lifetime budget-constrained VGI collaborative scheduling method for hydrogen fuel cell vehicle fleets provided in Embodiment 1 above. The system includes: The first acquisition module is used to acquire the capability message sequence of each vehicle; The second acquisition module is used to acquire power service request sequences from external power grids or microgrids; Establish a module to establish lifetime budget constraints and fuel cell stack equivalent ripple constraints; The solution module is used to solve the power service request sequence and capacity message sequence based on lifetime budget constraints, stack equivalent ripple constraints, stack ramp rate constraints, ground energy storage power and energy constraints, and stack power operation window constraints, with the goal of minimizing a preset objective function, to obtain ground energy storage power, stack power, and local energy storage power. The scheduling module is used to distribute the corresponding fuel cell stack power and local energy storage power to each vehicle, and to distribute the ground energy storage power to the ground energy storage system of the microgrid, so that each vehicle and the ground energy storage system can adjust the corresponding actual output power to achieve VGI coordinated scheduling.

[0100] Regarding the system in the above embodiments, the specific manner in which each unit performs operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0101] Embodiment 4 of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the lifetime budget constraint VGI cooperative scheduling method for hydrogen fuel cell vehicle fleets described in Embodiment 1.

[0102] Embodiment 5 of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the lifetime budget constraint VGI cooperative scheduling method for hydrogen fuel cell vehicle fleets as described in Embodiment 1.

[0103] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0104] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0105] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0106] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0107] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for cooperative scheduling of VGI with lifetime budget constraint for hydrogen fuel cell vehicle group, characterized in that, The method comprises: obtaining a sequence of capability messages of each vehicle; obtaining a sequence of power service requests from an external power grid or microgrid; establishing a life budget constraint and a stack equivalent ripple constraint; solving, based on the life budget constraint, the stack equivalent ripple constraint, a stack ramp rate constraint, a ground energy storage power and energy constraint, and a stack power operating window constraint, to minimize a preset objective function according to the sequence of power service requests and the sequence of capability messages, to obtain a ground energy storage power, a stack power, and a local energy storage power; each vehicle adjusts the actual output power according to the stack power and the local energy storage power, and the ground energy storage system of the microgrid adjusts the actual output power according to the ground energy storage power to realize VGI collaborative scheduling.

2. The method of claim 1, wherein the sequence of capability messages comprises at least one of the following: a stack health state parameter, a remaining life budget, an allowed stack power ramp-up upper limit, an allowed stack equivalent ripple upper limit, a remaining hydrogen amount, a minimum stack power in an allowed stack operating power window, and a maximum stack power in the allowed stack operating power window.

3. The method of claim 1, wherein the sequence of power service requests comprises a vehicle stack power, a ground energy storage power, and a vehicle local energy storage power.

4. The method of claim 1, wherein the preset objective function is represented as follows:

5. The method of claim 1, wherein the life budget constraint is established according to the following formula:

6. The method of claim 1, wherein the stack equivalent ripple constraint is established according to the following formula:

7. The method of claim 4, wherein a budget consumption increment is calculated in combination with an actual output power of the stack, a vehicle start-stop indication, a temperature out-of-limit indication, and a power fluctuation, and the remaining life budget is updated according to the budget consumption increment. wherein, represents the objective function, represents the ground energy storage power at time k, represents the output power of the i-th vehicle's power pack at time k, represents the charge-discharge power of the i-th vehicle's local energy storage at time k, represents the power service request of the grid at time k, M represents the total number of vehicles, N represents the length of the prediction horizon, and k represents the sampling time index.

8. The method of claim 4, wherein when the preset objective function has no solution within a given prediction time domain length, a relaxation variable is introduced to at least one of a change in the stack output power, a minimum stack power in the allowed stack operating power window, a maximum stack power in the allowed stack operating power window, and a difference in the sequence of power service requests; the relaxation variable is added to the preset objective function according to a preset weight to form a new objective function. wherein, represents the incremental budget consumption of the i-th vehicle, represents the remaining life budget of the i-th vehicle at time k, and N represents the prediction horizon length.

9. The method of claim 1, further comprising: ​ wherein, represents the average output power of the i-th vehicle stack over the sliding window, represents the equivalent ripple of the i-th vehicle stack at time k, represents the allowed stack equivalent ripple upper limit, represents the output power of the i-th vehicle stack at time j, represents the sliding window size. ​ ​ ​ ​ ​ ​ ​ When it is detected that the micro-grid is decoupled from the external grid or the external grid loses voltage, each vehicle is evaluated according to the remaining life budget, the equivalent ripple of the stack, the remaining hydrogen amount, the communication quality between vehicles, and the state of charge of the on-board battery to obtain a total score of each vehicle, and the vehicle with the highest total score is selected as the master vehicle, and the remaining vehicles are slave vehicles; The master vehicle broadcasts the network parameter range corresponding to the stack ramp rate constraint, the equivalent ripple of the stack constraint, the life budget constraint, and the stack power operating window constraint to the slave vehicles, and the slave vehicles enter parallel operation after verifying locally that the above constraints are met, forming an autonomous micro-grid; When the communication quality between the master vehicle and the slave vehicles is lower than the communication quality threshold, the ground energy storage system is controlled to maintain the stability of the bus voltage and frequency of the autonomous micro-grid, and the vehicle side only executes the power regulation strategy under the stack ramp rate constraint.

10. The VGI collaborative scheduling method for a hydrogen fuel cell vehicle group under life budget constraints according to claim 9, characterized in that: evaluating each vehicle according to the remaining life budget, the equivalent ripple of the stack, the remaining hydrogen amount, the communication quality between vehicles, and the state of charge of the on-board battery to obtain a total score of each vehicle comprises: wherein, represents the i-th vehicle communication quality score, represents the normalized i-th vehicle k-th time instant remaining life budget, represents the normalized remaining hydrogen amount, represents the normalized i-th vehicle stack equivalent ripple at time instant k, is the i-th vehicle k-th time instant on-board battery state of charge, are the weights corresponding to each score indicator, respectively.​​​​ 11. A VGI collaborative scheduling system for a hydrogen fuel cell vehicle group under life budget constraints, which implements the VGI collaborative scheduling method for a hydrogen fuel cell vehicle group under life budget constraints according to any one of claims 1 to 10, and comprises: a first acquisition module configured to acquire a sequence of capability messages of each vehicle; a second acquisition module configured to acquire a sequence of power service requests from an external grid or a micro-grid; an establishment module configured to establish life budget constraints and equivalent ripple constraints of the stack; a solving module configured to solve, based on the life budget constraints, the equivalent ripple constraints of the stack, the stack ramp rate constraint, the ground energy storage power and energy constraint, and the stack power operating window constraint, a preset target function to obtain the ground energy storage power, the stack power, and the local energy storage power according to the sequence of power service requests and the sequence of capability messages; a scheduling module configured to respectively issue the corresponding stack power and local energy storage power to each vehicle, and issue the ground energy storage power to the ground energy storage system of the micro-grid, so that each vehicle and the ground energy storage system adjust the corresponding actual output power to achieve VGI collaborative scheduling.

12. An electronic device, comprising a processor and a storage medium; characterized in that: the storage medium is configured to store instructions; the processor is configured to operate according to the instructions to perform the steps of the VGI collaborative scheduling method for a hydrogen fuel cell vehicle group under life budget constraints according to any one of claims 1 to 10.

13. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the VGI collaborative scheduling method for a hydrogen fuel cell vehicle group under life budget constraints according to any one of claims 1 to 10.

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