Dual-source life collaborative energy management method and system for hybrid power system

Abstract

The invention discloses a dual-source life collaborative energy management method and system for a hybrid power system. The method comprises the steps that S1, a dual-power-source hybrid power system model is established; s2, analyzing dual-power-source life collaborative decline characteristics; and S3, proposing and verifying an energy management strategy considering dual-power-source life collaborative decline. And an accurate life decline model of the fuel cell and the power cell is established, and equivalent hydrogen consumption of life loss of the dual power sources is introduced into an equivalent fuel consumption minimization strategy, so that economical efficiency and durability are synergistically optimized. The coupling relation of life decline of the two power sources is effectively quantified and correlated, so that the double power sources cooperatively attenuate according to a preset proportion, premature failure of a single power source is avoided, and the overall service life of the system is remarkably prolonged. Key operation parameters and fuzzy control rules can be dynamically adjusted according to the real-time aging states of the fuel cell and the power cell, so that an energy management strategy is always matched with the actual attenuation characteristics of the system in the whole life cycle, and the long-term adaptability of control is improved.

Description

Technical Field

[0001] This invention relates to a dual-source lifetime coordinated energy management method and system for a hybrid power system, belonging to the field of energy management technology. Background Technology

[0002] The increasingly severe energy crisis and environmental problems are driving the rapid development of new energy vehicles. Among them, fuel cell vehicles, with their advantages of zero pollution and high energy conversion efficiency, have shown great potential. However, pure fuel cell systems have inherent defects such as soft output characteristics and slow dynamic response, and usually need to be combined with other power sources (such as batteries) to form a hybrid power system to compensate for these deficiencies. In such multi-energy systems, different power sources have significant differences in output characteristics, response speed, and lifespan degradation mechanisms. Therefore, developing a reasonable energy management strategy (EMS) to coordinate the energy distribution among various power sources has become a core challenge in fuel cell hybrid vehicle research.

[0003] Based on control principles, energy management strategies can be mainly divided into three categories: rule-based, optimization-based, and learning-based. Rule-based strategies make decisions using a pre-defined power allocation rule table combined with real-time system state parameters. Fuzzy rules, in particular, are more robust than deterministic rules because they can handle system uncertainties and nonlinear characteristics. Optimization-based strategies establish a cost function and solve for its minimum value under constraints to achieve better performance. The equivalent hydrogen consumption minimization strategy, as a classic local optimization method, can balance the energy distribution between the fuel cell and the power battery in real time, showing significant effects in reducing hydrogen consumption and suppressing power fluctuations, and has become a research focus. With the advancement of artificial intelligence, learning-based strategies, such as reinforcement learning and deep learning, show potential by automatically learning patterns from data for prediction and decision-making. However, their reliance on large amounts of data, computational complexity, and poor real-time performance limit their application in real-world vehicles.

[0004] In summary, current energy management research mainly revolves around improving system economics, or simultaneously optimizing both economics and durability. However, current strategies often have limitations: some strategies aim only at "optimal economics," reducing operating costs by minimizing equivalent hydrogen consumption, without fully considering the aging and deterioration of the power source; other strategies, while focusing on durability, often only optimize the single lifespan of fuel cells or power batteries, failing to consider the coupling relationship between their lifespan degradation. This limitation can easily lead to premature failure of a power source in the system, resulting in a sharp decline in system performance and a significant increase in maintenance costs.

[0005] Furthermore, existing multi-objective energy management strategies are insufficient in addressing the characteristic changes during the aging process of power sources. For example, as service time increases, the high-power output threshold of fuel cells decreases, while the internal resistance of the power battery increases. Power allocation rules designed based on the initial state are difficult to dynamically adapt to these aging characteristics, leading to the gradual failure of the original strategy and even exacerbating the risk of lifespan mismatch between the two power sources. Summary of the Invention

[0006] Purpose of the invention: To address the shortcomings of existing technologies, this invention provides a dual-source lifespan coordinated energy management method and system for hybrid power systems, enabling coordinated lifespan degradation of the two power sources and solving the problem of rapid performance degradation and increased costs caused by premature failure of one power source.

[0007] Technical solution: A dual-source lifetime coordinated energy management method for a hybrid power system, comprising the following steps:

[0008] S1. Establishment of a dual-power-source hybrid power system model: including the construction of a hybrid power system model, the establishment of a fuel cell life degradation model, and the establishment of a power battery life degradation model;

[0009] S2. Analysis of the synergistic degradation characteristics of dual power source lifetimes, including:

[0010] S201. Construct a multi-objective equivalent fuel consumption minimization strategy ECMS;

[0011] S202. Analysis of the synergistic degradation characteristics of dual power sources in the unaged state;

[0012] S203. Analysis of the synergistic degradation characteristics of dual power source lifespan under aging conditions;

[0013] S3. Proposal and verification of an energy management strategy considering the synergistic degradation of the lifespan of dual power sources.

[0014] Preferably, the fuel cell life degradation model established in S1 is specifically as follows:

[0015] Fuel cell performance degradation is closely related to start-stop, idling, load variation, and high-power operating conditions, as shown in the following mathematical expression:

[0016] (1)

[0017] In the formula, The cumulative voltage decay rate of the fuel cell over time t is the percentage decrease in voltage. To account for correction factors related to air pollution in actual use environments; , , and These represent the voltage attenuation ratios under start-up, idling, load changing, and high-power operating conditions at time t, respectively.

[0018] , , and Calculated using the following formula:

[0019] (2)

[0020] In the formula, This is the start / stop indicator for the fuel cell, where 0 indicates shutdown and 1 indicates startup. For the output power of the fuel cell, This refers to the output power threshold of the fuel cell under idling conditions. This refers to the output power threshold of the fuel cell under high-power operating conditions. Represents a moment, n and n-1 represent two adjacent moments.

[0021] Preferably, the power battery life degradation model established in S1 is specifically as follows:

[0022] Model of capacity decay rate of power battery as follows:

[0023] (3)

[0024] In the formula, The charge / discharge rate of the power battery. The capacity decay rate at different rates;

[0025] Ignoring the effects of storage on aging, a single charge-discharge test includes one charge and one discharge cycle, so the time required for one cycle of aging is:

[0026] (4)

[0027] In the formula, The time required for one cycle of aging. The time required for one charge or discharge cycle. The capacity is measured during capacity calibration before each cycle of aging. This refers to the charging and discharging current.

[0028] The cycle capacity decay rate is converted to the time-dependent capacity decay rate using the following expression:

[0029] The cyclic capacity decay rate in equation (3) is converted into the time-dependent capacity decay rate, as shown in the following expression:

[0030] (5)

[0031] In the formula, This represents the rate of degradation of the power battery per second.

[0032] The battery's lifespan is considered to have reached its end when its capacity degrades by 20%. Assuming the nominal capacity of the lithium iron phosphate battery used is 20 Ah, then the percentage degradation per second of the battery is:

[0033]

[0034] In the formula, The percentage of battery degradation per second;

[0035] Based on formulas (3) and (6), the suitable life degradation model for power batteries is as follows:

[0036]

[0037] Preferably, S201 specifically includes:

[0038] The Equivalent Fuel Consumption Minimization Strategy (ECMS) is a local optimization strategy. Its key feature is its ability to dynamically correlate battery energy and hydrogen consumption through an equivalent conversion mechanism, achieving instantaneous optimization of the global problem. Its core idea is to use an equivalent factor to convert the energy absorption and release during battery charging and discharging into hydrogen consumption and compensation. In hybrid power system energy management, not only system economy but also durability must be considered. Therefore, the energy allocation problem of a fuel cell hybrid power system is transformed into:

[0039] (8)

[0040] In the formula, Represents the total equivalent hydrogen consumption. This represents the direct hydrogen consumption of fuel cells. Represents the equivalent hydrogen consumption of the power battery. The equivalent hydrogen consumption for fuel cell lifespan loss. The equivalent hydrogen consumption for the loss of power battery life;

[0041] A compensation coefficient is introduced when calculating the equivalent hydrogen consumption of the power battery. The purpose is to ensure that the power battery operates within a reasonable range and to prevent overcharging or over-discharging; the expression for the compensation coefficient is:

[0042] (9)

[0043] In the formula, The equilibrium coefficient; SOCL SOC H These are the lower and upper limits of the SOC (State of Charge) of the power battery, respectively.

[0044] Equivalent hydrogen consumption of power batteries The calculation method is as follows:

[0045] (10)

[0046] In the formula, Represents the power battery capacity. It is the average hydrogen consumption of the fuel cell. It is the average charging efficiency of the power battery; It is the average power of the fuel cell. It refers to the discharge efficiency of the power battery; It refers to the charging efficiency of the power battery; It is the average discharge efficiency of the power battery;

[0047] During the operation of the power battery, the real-time charging and discharging efficiency is calculated using equation (11);

[0048] (11)

[0049] In the formula, This represents the internal resistance of the power battery during discharge. Represents the internal resistance of the power battery during charging; when When ≥0, it indicates that the power battery is in a discharging state; when When the value is less than 0, it indicates that the power battery is in a charging state. The square of the total open-circuit voltage of the battery pack;

[0050] Calculated using equation (12):

[0051] (12)

[0052] In the formula, This represents the maximum output power of the fuel cell. The initial purchase cost of a 1 kW fuel cell, For the loss of fuel cells, The price of hydrogen, 10% is the end-of-life standard. This represents the proportion of the recyclable value of the fuel cell.

[0053] Calculated using equation (13):

[0054] (13)

[0055] In the formula, The rated energy of the power battery, The initial purchase cost of a 1 kWh power battery. The loss rate of the power battery, This represents the proportion of the recyclable value of the power battery. The price of hydrogen is 20%, which is the end-of-life standard.

[0056] Calculated by equation (14):

[0057] (14)

[0058] In the formula, and These represent the number of power batteries connected in series and in parallel, respectively. This refers to the rated voltage of the power battery. This refers to the rated capacity of the power battery.

[0059] To ensure the proper functioning of the entire optimization system, the following constraints need to be added to the optimization problem, expressed as follows:

[0060] (15)

[0061] In the formula, , , These represent the power requirements of the entire vehicle, the power of the battery, and the power of the fuel cell system, respectively. and These represent the maximum and minimum output power of the power battery, respectively. This is the maximum output power of the fuel cell system. and These represent the minimum and maximum power variation ranges of the fuel cell system, respectively. This is the difference between the current fuel cell output power and the previous fuel cell output power.

[0062] In a preferred embodiment, S202 specifically includes:

[0063] The characteristics of fuel cells under different power levels under multi-objective optimization were investigated, and the impact of fuel cell output power on overall hydrogen consumption was analyzed to determine the optimal output power in lifetime co-optimization. The specific optimization method is as follows:

[0064] S2021. Calculate the degradation rate of fuel cells and power batteries respectively using the life degradation models of fuel cells and power batteries.

[0065] S2022. Based on the lifespan degradation of the dual power sources in S2021 above, simulate and obtain the lifespan-coordinated vehicle speed point, coordinated hydrogen consumption, and coordinated error under different fuel cell output powers:

[0066] Different fuel cell output powers were selected, and each output power was simulated independently. The simulation was run under comprehensive operating conditions, and the life degradation of the fuel cell and the power battery was accumulated in real time. When the ratio of life loss of the two met the preset coordination error threshold, the number of coordinated vehicle speed points consumed at this time was recorded. The total equivalent hydrogen consumption during the entire coordination process was calculated. The hydrogen consumption under different power levels was standardized to the same driving distance benchmark using the equivalence coefficient. The coordination error was calculated from the difference in the life loss ratio at the end of the simulation.

[0067] The preset cooperative error threshold of 0.0001% is used as a constraint. ;

[0068] Equivalent hydrogen consumption = Actual hydrogen consumption × Equivalent coefficient;

[0069] Cooperative error = ;

[0070] S2023. Considering the critical settings of fuel cells and power batteries under collaborative conditions; when the SOC of the power battery reaches 80%, the output power of the fuel cell is set to the critical value of 3.6 kW for idling conditions, and when the SOC drops to 40%, the output power of the fuel cell is set to the critical value of 21.7 kW for high-power conditions.

[0071] Based on the above optimization method, the original constraint equation (15) is modified, and the optimized constraint is:

[0072] (16)

[0073] In the formula, This is due to the loss of the power battery; For losses in fuel cells; This is the cooperative error;

[0074] By setting a collaborative error threshold, the output power of the power battery and the output power of the fuel cell under the collaborative degradation of the lifespan of the two power sources under multiple objectives are obtained; the power of the fuel cell includes the constant output power during the collaborative process and the critical idle power of the fuel cell to prevent overcharging of the power battery.

[0075] The equivalent coefficients are obtained based on the ratio between the cooperative speed points at each power level and the maximum value of the cooperative speed points at all power levels. The number of constant power speed points is then equivalently calculated based on the maximum value. The calculation method for the equivalent coefficients is as follows:

[0076] (17)

[0077] In the formula, Equivalent coefficient; The number of coordinated vehicle speed points corresponding to different power levels; This represents the maximum number of coordinated vehicle speed points under different power levels.

[0078] In a preferred embodiment, S203 specifically includes:

[0079] S2031, Aging Characteristics Analysis of Fuel Cells and Power Batteries;

[0080] Given the aging state of a fuel cell, the method for calculating the high-power critical point of a fuel cell is as follows:

[0081] (18)

[0082] In the formula, For fuel cells, the high-power criticality, The aging degree of the fuel cell;

[0083] Battery aging also affects external characteristics, primarily manifested in capacity decay and increased internal resistance. Capacity decay curves and internal resistance curves were obtained through battery aging experiments. The capacity decay of the battery with the number of cycles during aging was transformed into the change in battery capacity over time (s). The calculation is as follows:

[0084] (19)

[0085] In the formula, Let i be the initial capacity of the power battery, and i be the simulation step size.

[0086] Based on this, the aging rate of the battery Represented as:

[0087] (20)

[0088] Based on the aging state of the power battery, the internal resistance is calculated using equation (20):

[0089] (twenty one)

[0090] In the formula, This refers to the internal resistance of the power battery when it is not yet aged.

[0091] S2032. Analysis of the Co-degradation Characteristics of Dual Power Source Lifespan Considering Aging: In actual use, the performance of fuel cells and power batteries will degrade with increasing operating time. Considering the adjustment of the co-degradation of dual power source lifespan after actual multi-objective optimization, so that the degradation ratio of fuel cells and power batteries is always maintained at 1:2, the co-operation speed point is equivalent to the maximum value to analyze the impact of fuel cell output power on hydrogen consumption and dual power source lifespan under multi-objective optimization during system degradation. The relationship curve between hydrogen consumption and fuel cell power during the multi-objective coordination process during system degradation is obtained. Comparing the relationship curves of the non-aged state and the aged state, it can be seen that the optimal power output range also changes due to the change of the high-power critical point. Therefore, when formulating energy management strategies, it is necessary to dynamically adjust the optimal output power range of fuel cells according to the aging state.

[0092] In a preferred embodiment, S3 specifically includes:

[0093] S301. Determine the range of input and output variables and construct a language library and rule library, and then propose a dual-power source lifetime collaborative decay strategy based on fuzzy control.

[0094] S302. Verify the dual-power source lifetime co-degradation strategy based on fuzzy control during both the unaged and aged periods.

[0095] Preferably, S301 specifically includes:

[0096] The range of input and output variables is determined:

[0097] The required power P of the whole vehicle re With the power battery SOC as an input variable, the fuel cell output power P fc P is the output variable. re The universe of discourse of is [-60, 70] kW, and the universe of discourse of SOC is [30, 100]%, P fc The domain of discourse is [0, 30] kW;

[0098] Language library and rule base construction:

[0099] The fuzzy subset of the vehicle's required power is set as {Very Low (RVL), Low (RL), Medium (RM), High (RH), Very High (RVH)}; the fuzzy subset of the power battery's SOC is set as {Very Low (SVL), Low (SL), Medium (SM), High (SH), Very High (SVH)}; and the fuzzy subset of the fuel cell's output power is set as {Very Low (FVL), Low (FL), Medium (FM), High (FH), Very High (FVH)}.

[0100] The range of the demand power fuzzy subset setting is {[-30,-10], [-20,10], [5,30], [25,50], [45,70]}; the range of the power battery SOC fuzzy subset setting is {[30,45], [40,50], [47,58.9], [54,88], [85,100]}; the range of the fuel cell output power fuzzy subset setting is {[0,3.6], [3,13], [12,22], [20,26], [24,30]};

[0101] Secondly, triangular membership functions and trapezoidal membership functions are selected based on the characteristics of the input and output quantities, thereby constructing fuzzy rules to achieve the optimal power output of the fuel cell.

[0102] A system for implementing a dual-source lifetime coordinated energy management method for hybrid power systems includes a data acquisition module, a model building module, an aging state calculation module, a fuzzy rule adaptive module, and an energy management module.

[0103] The data acquisition module is used to collect vehicle speed, power demand, SOC, voltage, and current in real time.

[0104] The model building module is used to build life degradation models for the entire vehicle powertrain system and dual power sources;

[0105] The aging state calculation module is used to calculate the aging degree of fuel cells and power batteries in real time, quantify the life decay state of dual power sources, and provide aging basis for fuzzy rule adaptive adjustment and energy management strategy optimization.

[0106] The fuzzy rule adaptive module is used to adaptively adjust the fuzzy variable domain, fuzzy rule base content, and membership function according to the aging state, vehicle execution parameters, and dual power source coordination error.

[0107] The energy management module is used to coordinate the power output of the fuel cell and the power battery according to control commands.

[0108] Beneficial Effects: This invention establishes a precise lifespan degradation model for fuel cells and power batteries, and introduces equivalent hydrogen consumption from the lifespan loss of both power sources into the equivalent fuel consumption minimization strategy, thus synergistically optimizing economy and durability. This method effectively quantifies and correlates the coupling relationship between the lifespan degradation of the two, enabling the two power sources to degrade synergistically according to a preset ratio, avoiding premature failure of a single power source, thereby significantly extending the overall lifespan of the system and achieving lifespan synergy and global optimization. It can dynamically adjust key operating parameters and fuzzy control rules based on the real-time aging status of fuel cells and power batteries, ensuring that the energy management strategy always matches the actual degradation characteristics of the system throughout its entire lifespan. This overcomes the problem of aging failure of traditional static strategies, improves the long-term adaptability of control, and achieves dynamic adaptation to aging while maintaining strategy effectiveness. While optimizing instantaneous fuel consumption, it also comprehensively considers the long-term costs brought about by lifespan degradation, achieving an optimal balance between immediate economy and long-term durability, helping to reduce the total operating cost of the vehicle throughout its entire lifespan, and balancing economic durability with improved overall efficiency. The control architecture based on fuzzy logic does not rely on complex online optimization, has low computational load and fast response, meets the real-time requirements of real vehicles, and the modular system design also facilitates engineering integration and promotion, ensuring the potential of real-time control and engineering applications. Attached Figure Description

[0109] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0110] Figure 1 This is a flowchart of the method of the present invention;

[0111] Figure 2 This is a power-voltage curve for a fuel cell.

[0112] Figure 3 The graph shows the (a) vehicle speed versus (b) power demand curves under combined operating conditions.

[0113] Figure 4 (a) SOC curve and (b) power curve plots from multi-objective ECMS simulation;

[0114] Figure 5 To coordinate the adjustment of (a) SOC curve and (b) power curve when the battery is not aged;

[0115] Figure 6 This is a graph showing the relationship between hydrogen consumption and fuel cell power in the hybrid system during the collaborative process.

[0116] Figure 7The graphs show the changes in (a) capacity and (b) internal resistance of the power battery as a function of aging.

[0117] Figure 8 The graphs show the relationship between hydrogen consumption and fuel cell power in the hybrid system during the collaborative process under different aging conditions. (a) represents the 2% aging state, and (b) represents the 4% aging state.

[0118] Figure 9 For the fuzzy membership function (a)P re Membership function, (b) SOC membership function, (c) P fc Membership functions and (d) fuzzy rule map;

[0119] Figure 10 The simulation comparison diagrams of fuzzy collaborative control and multi-objective ECMS strategy before aging are shown. (a) is the power curve and (b) is the SOC curve.

[0120] Figure 11 A comparison of the degradation rates of (a) fuzzy collaborative control strategy and (b) multi-objective ECMS strategy with dual power sources before aging;

[0121] Figure 12 For system aging, 2% of (a) SOC and (b) P fc Membership function graph;

[0122] Figure 13 For system aging, 4% of (a) SOC and (b) P fc Membership function graph;

[0123] Figure 14 The simulation comparison diagrams of the fuzzy collaborative control strategy and the multi-objective ECMS strategy for the system aging by 2% are shown in the figure. (a) is the power curve and (b) is the SOC curve.

[0124] Figure 15 A comparison of the degradation rates of (a) fuzzy collaborative control strategy and (b) multi-objective ECMS strategy with dual power source at 2% system aging;

[0125] Figure 16 The simulation comparison diagrams of fuzzy collaborative control and multi-objective ECMS strategy for system aging of 4% are shown. (a) is the power curve and (b) is the SOC curve.

[0126] Figure 17 A comparison of the degradation rates of (a) fuzzy collaborative control and (b) multi-objective ECMS strategy dual power source for a system aging of 4%. Detailed Implementation

[0127] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0128] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0129] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0130] like Figure 1 As shown, a dual-source lifetime coordinated energy management method for a hybrid power system includes the following steps:

[0131] S1. Establishment of a dual-power-source hybrid power system model: including the construction of a hybrid power system model, the establishment of a fuel cell life degradation model, and the establishment of a power battery life degradation model;

[0132] In this embodiment, the hybrid power system model construction method adopts the process described in the patent "A Hybrid System Energy Management Method and Device Considering Battery Lag" (CN119058493B), which includes "S1 Fuel Cell Hybrid Power System Model Construction, including Powertrain System Construction, Fuel Cell Model Construction, Power Battery Model Construction, and Drive Motor Model Construction".

[0133] The fuel cell life degradation model in S1 is specifically established as follows:

[0134] Fuel cell performance degradation is closely related to start-stop, idling, load variation, and high-power operating conditions, as shown in the following mathematical expression:

[0135] (1)

[0136] In the formula, The cumulative voltage decay rate of the fuel cell over time t is the percentage decrease in voltage. To account for correction factors related to air pollution in actual use environments; , , and These represent the voltage attenuation ratios under start-stop, idling, load changing, and high-power operating conditions at time t.

[0137] , , and Calculated using the following formula:

[0138] (2)

[0139] In the formula, This is the start / stop indicator for the fuel cell, where 0 indicates shutdown and 1 indicates startup. For the output power of the fuel cell, This refers to the output power threshold of the fuel cell under idling conditions. This refers to the output power threshold of the fuel cell under high-power operating conditions. Represents a moment, n and n-1 represent two adjacent moments.

[0140] Generally, a fuel cell cell voltage greater than 0.85 V is considered to be in idle condition, and a fuel cell stack voltage less than 0.7 V is considered to be in high-power condition. The relationship between the output voltage of the fuel cell cell and the power of the fuel cell stack used in this invention is as follows: Figure 2 As shown. kW, kW.

[0141] The power battery life degradation model in S1 is specifically established as follows:

[0142] Within the normal DOD range, the impact of DOD on capacity decay is minimal. Therefore, the effects of depth of charge / discharge and temperature are ignored, and only the charge / discharge effect is considered. Aging experiments from 0.5C to 1.5C were conducted to fit the results. The capacity decay rate f at different rates was obtained from the test results, as shown in Table 1.

[0143] Table 1 Capacity decay rate at different rates

[0144]

[0145] The capacity degradation rate model for power batteries is as follows:

[0146] (3)

[0147] In the formula, The charge / discharge rate of the power battery. The capacity decay rate at different rates;

[0148] Ignoring the effects of storage on aging, a single charge-discharge test includes one charge and one discharge cycle, so the time required for one cycle of aging is:

[0149] (4)

[0150] In the formula, The time required for one cycle of aging. The time required for one charge or discharge cycle. The capacity is measured during capacity calibration before each cycle of aging. This is the charging and discharging current.

[0151] The cycle capacity decay rate is converted to the time-dependent capacity decay rate using the following expression:

[0152] The cyclic capacity decay rate in equation (3) is converted into the time-dependent capacity decay rate, as shown in the following expression:

[0153] (5)

[0154] In the formula, This represents the rate of degradation of the power battery per second.

[0155] The battery's lifespan is considered to have reached its end when its capacity degrades by 20%. Assuming the nominal capacity of the lithium iron phosphate battery used is 20 Ah, then the percentage degradation per second of the battery is:

[0156]

[0157] In the formula, This represents the percentage of battery degradation per second.

[0158] Based on formulas (3) and (6), the suitable life degradation model for power batteries is as follows:

[0159]

[0160] S2. Analysis of the synergistic degradation characteristics of dual power source lifetimes:

[0161] Current mainstream energy management strategies have significant limitations: they either focus solely on minimizing hydrogen consumption (economic efficiency) or only optimize fuel cell durability, completely ignoring the lifespan synergy of the two power sources. This single-objective thinking directly leads to a severe mismatch in their lifespans. As core power units, the lifespan matching of the two power sources directly determines the system's reliability. The aging of the two power sources directly alters their core characteristics, and if energy management strategies are not adjusted based on synergistic degradation characteristics, they will gradually fail. Therefore, the following analysis examines the lifespan synergy characteristics of the two power sources:

[0162] S201. Construct a multi-objective equivalent fuel consumption minimization strategy ECMS;

[0163] The Equivalent Fuel Consumption Minimization Strategy (ECMS) is a local optimization strategy characterized by its ability to dynamically correlate battery energy and hydrogen consumption through an equivalent conversion mechanism, achieving instantaneous optimization of the global problem. Its core idea is to use an equivalent factor to convert the absorption and release of electrical energy during battery charging and discharging into hydrogen consumption and compensation. In hybrid power system energy management, not only system economy but also durability need to be considered. Therefore, the energy allocation problem of a fuel cell hybrid power system is transformed into:

[0164] (8)

[0165] In the formula, Represents the total equivalent hydrogen consumption. This represents the direct hydrogen consumption of fuel cells. Represents the equivalent hydrogen consumption of the power battery. The equivalent hydrogen consumption for fuel cell lifespan loss. This is the equivalent hydrogen consumption due to the loss of power battery life.

[0166] A compensation coefficient is introduced when calculating the equivalent hydrogen consumption of the power battery. The purpose is to ensure that the power battery operates within a reasonable range and to prevent overcharging or over-discharging. The expression for the compensation coefficient is:

[0167] (9)

[0168] In the formula, The equilibrium coefficient; SOC L SOC H These represent the lower and upper limits of the SOC (State of Charge) of the power battery, respectively.

[0169] Equivalent hydrogen consumption of power batteries The calculation method is as follows:

[0170] (10)

[0171] In the formula, Represents the power battery capacity. It is the average hydrogen consumption of the fuel cell. It is the average charging efficiency of the power battery; It is the average power of the fuel cell. It refers to the discharge efficiency of the power battery; It refers to the charging efficiency of the power battery; It is the average discharge efficiency of the power battery.

[0172] During the operation of the power battery, the real-time charging and discharging efficiency is calculated using equation (11).

[0173] (11)

[0174] In the formula, This represents the internal resistance of the power battery during discharge. This represents the internal resistance of the power battery during charging. When... When ≥0, it indicates that the power battery is in a discharging state; when When the value is less than 0, it indicates that the power battery is in a charging state. The square of the total open-circuit voltage of the battery pack;

[0175] Calculated using equation (12):

[0176] (12)

[0177] In the formula, This represents the maximum output power of the fuel cell. The initial purchase cost of a 1 kW fuel cell, For the loss of fuel cells, The price of hydrogen, 10% is the end-of-life standard. This represents the proportion of the recyclable value of a fuel cell.

[0178] Calculated using equation (13):

[0179] (13)

[0180] In the formula, The rated energy of the power battery, The initial purchase cost of a 1 kWh power battery. The loss rate of the power battery, This represents the proportion of the recyclable value of the power battery. The price of hydrogen is 20%, which is the end-of-life standard.

[0181] Calculated by equation (14):

[0182] (14)

[0183] In the formula, and These represent the number of power batteries connected in series and in parallel, respectively. This refers to the rated voltage of the power battery. This refers to the rated capacity of the power battery.

[0184] To ensure the proper functioning of the entire optimization system, the following constraints need to be added to the optimization problem, expressed as follows:

[0185] (15)

[0186] In the formula, , , These represent the power requirements of the entire vehicle, the power of the battery, and the power of the fuel cell system, respectively. and These represent the maximum and minimum output power of the power battery, respectively. This is the maximum output power of the fuel cell system. and These represent the minimum and maximum power variation ranges of the fuel cell system, respectively. This is the difference between the current fuel cell output power and the previous fuel cell output power.

[0187] K-means clustering algorithm was used to obtain three typical operating conditions: low speed, medium speed, and high speed. Based on the average proportion of the three operating conditions, a comprehensive test condition for the actual vehicle was formed using random sampling without replacement. The speed and power curves corresponding to the comprehensive operating condition are shown below. Figure 3 As shown.

[0188] The initial SOC of the power battery was set to 0.6, and the simulated SOC curve and the power curves of the power battery and fuel cell were obtained as follows: Figure 4 As shown. According to Figure 4 It can be seen that the SOC value varies between 57.2% and 61.9%. Within the constraint range of 30% to 90%, the vehicle's load power demand is mainly provided by the fuel cell, while the power battery, as an auxiliary power source, only plays a "peak shaving and valley filling" role. Furthermore, the lifespan degradation of the fuel cell and power battery in the multi-objective optimization simulation was calculated, yielding values ​​of 0.0128% and 0.0197%, respectively. The results show that there is a lack of coordination in their lifespan degradation, with a non-coordination rate of approximately 0.0059%. This is mainly due to the initial start-up of the fuel cell and the fact that the vehicle's load power demand is primarily provided by the fuel cell.

[0189] S202. Analysis of the synergistic degradation characteristics of dual power sources in the unaged state;

[0190] Specifically, S202 is as follows:

[0191] The characteristics of fuel cells under different power levels under multi-objective optimization were investigated, and the impact of fuel cell output power on overall hydrogen consumption was analyzed to determine the optimal output power in lifetime co-optimization. The specific optimization method is as follows:

[0192] S2021. Calculate the degradation rate of fuel cells and power batteries respectively using the life degradation models of fuel cells and power batteries.

[0193] S2022. Based on the lifespan degradation of the dual power sources in S2021 above, simulate and obtain the lifespan-coordinated vehicle speed point, coordinated hydrogen consumption, and coordinated error under different fuel cell output powers:

[0194] Different fuel cell output powers were selected, and each output power was simulated independently. The simulation was run under comprehensive operating conditions, and the life degradation of the fuel cell and the power battery was accumulated in real time. When the ratio of life loss of the two met the preset coordination error threshold, the number of coordinated vehicle speed points consumed at this time was recorded. The total equivalent hydrogen consumption during the entire coordination process was calculated. The hydrogen consumption under different power levels was standardized to the same driving distance benchmark using the equivalence coefficient. The coordination error was calculated from the difference in the life loss ratio at the end of the simulation.

[0195] The preset cooperative error threshold of 0.0001% is used as a constraint. ;

[0196] Equivalent hydrogen consumption = Actual hydrogen consumption × Equivalent coefficient;

[0197] Cooperative error = ;

[0198] S2023. Consider the critical settings for fuel cells and power batteries under coordinated operation. When the state of charge (SOC) of the power battery reaches 80%, the output power of the fuel cell is set to the critical value of 3.6 kW for idle conditions. When the SOC drops to 40%, the output power of the fuel cell is set to the critical value of 21.7 kW for high-power conditions.

[0199] Based on the above optimization method, the original constraint equation (15) is modified, and the optimized constraint is:

[0200] (16)

[0201] In the formula, This is due to the loss of the power battery; For losses in fuel cells; This is the cooperative error.

[0202] In this embodiment, the cooperative error threshold is set to 0.0001%. Taking 6 kW as an example, the output power of the power battery and the output power of the fuel cell under the cooperative degradation of the dual power source lifetime under multiple objectives are obtained as follows: Figure 5 As shown. From Figure 5 It can be seen that the power of the fuel cell includes the constant output power during the collaborative process and the critical power of the fuel cell output at idle speed to prevent the power battery from overcharging.

[0203] To better compare the impact of different constant output power of fuel cells on hydrogen consumption and the lifespan of dual power sources, equivalent coefficients were obtained based on the ratio of the cooperative vehicle speed point at each power level to the maximum value of the cooperative vehicle speed point across all power levels. The number of constant power vehicle speed points was then equivalently calculated based on the maximum value. The calculation method for the equivalent coefficients is as follows:

[0204] (17)

[0205] In the formula, Equivalent coefficient; The number of coordinated vehicle speed points corresponding to different power levels; This represents the maximum number of coordinated vehicle speed points under different power levels.

[0206] The relationship curve between hydrogen consumption of the hybrid system and fuel cell power during the synergistic process is obtained from Table 2 and the equivalent coefficient of equation (17). Figure 6 As shown. From Figure 6 As can be seen, when the output power of the fuel cell is in the range of 8~19 kW, the hydrogen consumption of the hybrid system is relatively small.

[0207] Based on the dual-power-source lifespan coordination strategy formulated above, the hydrogen consumption of the fuel cell at different constant power levels is shown in Table 2:

[0208] Table 2 Hydrogen consumption after lifetime co-decay under single objective

[0209]

[0210] Table 2 and equivalent coefficients were used to obtain the equivalent hydrogen consumption at different constant power levels, revealing the relationship between hydrogen consumption in the hybrid system and fuel cell output power. Figure 6 As can be seen from this, when the output power of the fuel cell is in the range of 12~19 kW, the hydrogen consumption of the hybrid system is relatively small.

[0211] S203. Analysis of the synergistic degradation characteristics of dual power source lifespan under aging conditions;

[0212] Specifically, S203 is:

[0213] S2031, Aging Characteristics Analysis of Fuel Cells and Power Batteries;

[0214] The main external characteristic of fuel cell aging is its terminal voltage. With increasing service time, the terminal voltage of the fuel cell under steady-state operating conditions tends to decrease, leading to a reduction in output power. Considering that the maximum output power of the fuel cell after aging will affect its critical output capability, Table 3 presents the maximum output power and high-power criticality under different aging states of the fuel cell. It can be seen from the table that as the aging of the fuel cell intensifies, the high-power output criticality gradually decreases.

[0215] Table 3. Changes in fuel cell performance parameters with aging

[0216]

[0217] Given the aging state of a fuel cell, the method for calculating the high-power critical point of a fuel cell is as follows:

[0218] (18)

[0219] In the formula, For fuel cells, the high-power criticality, This refers to the aging degree of the fuel cell.

[0220] Battery aging also affects external characteristics, mainly manifested in capacity decay and internal resistance increase. Capacity degradation curves and internal resistance curves are obtained through battery aging experiments, such as... Figure 7 As shown, the capacity decay of the power battery during aging with the number of cycles is converted into the change of power battery capacity over time (s). The calculation is as follows:

[0221] (19)

[0222] In the formula, Let be the initial capacity of the power battery, and i be the simulation step size.

[0223] Based on this, the aging rate of the battery Represented as:

[0224] (20)

[0225] Based on the aging state of the power battery, the internal resistance is calculated using equation (20):

[0226] (twenty one)

[0227] In the formula, This refers to the internal resistance of the power battery when it is not yet aged.

[0228] S2032. Analysis of the synergistic degradation characteristics of dual power sources considering aging:

[0229] In actual use, the performance of both the fuel cell and the power battery will degrade with increasing operating time. Considering the need for coordinated degradation adjustment of the dual power source lifespan after multi-objective optimization, the degradation ratio of the fuel cell and the power battery is always maintained at 1:2. Table 4 shows the hydrogen consumption when the system degradation is 2%, i.e., the fuel cell degradation is 2% and the power battery degradation is 4%.

[0230] Table 4. Hydrogen consumption after co-decline when the system decays by 2%.

[0231]

[0232] Simulation of co-degradation under aging conditions:

[0233] In actual use, the fuel cell reaches its end of life when its degradation rate reaches 10% and the power battery reaches 20%. Considering the synergistic degradation relationship between the two power sources, the degradation ratio of the fuel cell and the power battery should be maintained at 1:2. Figure 8 The curves showing the relationship between hydrogen consumption and fuel cell power during the coordinated process at system degradation rates of 2% and 4% are presented. (Comparison) Figure 6 and Figure 8 It can be seen that the change in the high-power critical point leads to a change in the optimal power output range. The optimal power output range of the fuel cell changes from the initial 8~19 kW to 9~16 kW under 4% aging conditions, a slight decrease in range. This is mainly due to the performance degradation caused by the aging of various components within the system. Therefore, when formulating energy management strategies, it is necessary to dynamically adjust the optimal output power range according to the aging state.

[0234] S3. Proposal and verification of an energy management strategy considering the synergistic degradation of the lifespan of dual power sources;

[0235] S301. Determine the range of input and output variables and construct a language library and rule library, and then propose a dual-power source lifetime collaborative decay strategy based on fuzzy control.

[0236] The range of input and output variables is determined:

[0237] The required power P of the whole vehicle re With the power battery SOC as an input variable, the fuel cell output power P fc P is the output variable. re The universe of discourse of is [-60, 70] kW, and the universe of discourse of SOC is [30, 100]%, P fc The domain of discourse is [0, 30] kW.

[0238] Language library and rule base construction:

[0239] The fuzzy subset of the vehicle's required power is set as {Very Low (RVL), Low (RL), Medium (RM), High (RH), Very High (RVH)}; the fuzzy subset of the power battery's SOC is set as {Very Low (SVL), Low (SL), Medium (SM), High (SH), Very High (SVH)}; and the fuzzy subset of the fuel cell's output power is set as {Very Low (FVL), Low (FL), Medium (FM), High (FH), Very High (FVH)}.

[0240] The range of the demand power fuzzy subset setting is {[-30,-10], [-20,10], [5,30], [25,50], [45,70]}; the range of the power battery SOC fuzzy subset setting is {[30,45], [40,50], [47,58.9], [54,88], [85,100]}; the range of the fuel cell output power fuzzy subset setting is {[0,3.6], [3,13], [12,22], [20,26], [24,30]};

[0241] Secondly, triangular membership functions and trapezoidal membership functions are selected based on the characteristics of the input and output quantities, thereby constructing fuzzy rules to achieve the optimal power output of the fuel cell.

[0242] To better leverage the characteristics of fuel cells and power batteries, the following rules are designed:

[0243] (1) When the SOC of the power battery is low, in order to prevent the power from being over-discharged, the output power of the fuel cell needs to be maintained in a high range. This will meet the power requirements of the vehicle while simultaneously replenishing the power battery, thus prompting the SOC of the power battery to recover quickly.

[0244] (2) When the SOC of the power battery is high, in order to prevent the power battery from being overcharged, the fuel cell should be kept in a low power state as much as possible, and the power battery should provide part of the power so that the SOC of the power battery can drop to a safe range as soon as possible.

[0245] (3) In order to achieve synergy between the lifespans of the two power sources as soon as possible, the lifespan loss of the fuel cell should be minimized, and the fuel cell should be operated in a non-idling and non-high-power state.

[0246] (4) In order to ensure the economy of the whole vehicle, the fuel cell should be operated in the low hydrogen power consumption range as much as possible, that is, the optimal power output range in the previous chapter.

[0247] The fuzzy rules formulated based on the above principles are shown in Table 5, and corresponding fuzzy rule maps have been developed, such as... Figure 9 As shown:

[0248] Table 5 Fuzzy Rule Table

[0249]

[0250] S302. Verify the dual-power source lifetime co-degradation strategy based on fuzzy control during both the unaged and aged periods:

[0251] Validation of the dual-power source lifespan synergistic degradation strategy before aging:

[0252] Based on the aforementioned fuzzy rules, a co-simulation of the lifespan of the two power sources before aging was performed. The power and SOC curves under fuzzy co-control and multi-objective ECMC strategies were obtained, as shown below. Figure 10 As shown in the diagram. Comparing the two, it can be seen that compared to the multi-objective ECMS strategy, the fuel cell output power of the fuzzy cooperative control strategy changes relatively more slowly, while the SOC fluctuation is relatively larger. This is because the multi-objective ECMS strategy primarily utilizes the fuel cell, leading to its rapid lifespan degradation, while the fuzzy cooperative control strategy aims to protect the fuel cell from excessive degradation. Furthermore, Figure 11 The degradation rate curves of fuel cells and power batteries under two strategies are presented. It should be noted that the fuel cell reaches the end of its lifespan at 10% degradation, while the power battery reaches its end of lifespan at 20% degradation; therefore, the fuel cell degradation rate is doubled. The fuzzy collaborative control strategy achieved its collaborative objective with a collaborative error of 0.00004%, while the ECMS strategy had a collaborative error of 0.0062%. Figure 11 It can also be seen that the two strategies generated a large cooperative error of about 0.0039% at the initial operating point, which is due to the start-up and shutdown of the fuel cell.

[0253] To further compare the optimization effects of the two strategies, Table 6 presents the results of the two strategies on fuel cell hydrogen consumption C. fc Equivalent hydrogen consumption of power battery C bat Fuel cell lifespan reduction and hydrogen consumption C fc_loss and the reduction in power battery life and hydrogen consumption C bat_loss Data Comparison. The table shows that the total hydrogen consumption for the fuzzy collaborative strategy and the multi-objective ECMS strategy are 2943.5 g and 3600.1 g, respectively, a relative reduction of 18%. The power curves suggest this reduction may be due to the relatively slower fluctuations in fuel cell power. In this case, the decrease in total hydrogen consumption stems from the reduction in hydrogen consumption due to fuel cell lifespan loss. Therefore, fuel cell loss data for both strategies are presented here, as shown in Table 7. The table shows that the fuel cell losses for the fuzzy collaborative strategy and the multi-objective ECMS strategy are 0.0091% and 0.013%, respectively, a relative reduction of 30%. This is mainly attributed to the reduction in variable load losses, which is a relative reduction of 43%.

[0254] Table 6 Comparison of Fuzzy Collaborative Strategy and Multi-Objective ECMS Hydrogen Consumption

[0255]

[0256] Table 7 Comparison of Fuzzy Cooperative Strategy and Multi-Objective ECMS Fuel Cell Lifetime

[0257]

[0258] Validation of the dual-power source lifetime synergistic degradation strategy during aging:

[0259] As the system ages, the optimal power output range of the fuel cell will change. System aging will also affect the characteristics of the dual power sources. Considering that system aging only affects the upper limit of the optimal power output range of the fuel cell, and has little impact on the lower limit, and also considering the impact of battery aging-induced changes in capacity and internal resistance on the battery degradation rate, the SOC and PFC membership functions of the battery and fuel cell are adjusted here. The SOC membership functions and PFC membership functions after 2% and 4% system aging are as follows: Figure 12 and Figure 13 As shown.

[0260] Based on the membership functions of different aging states of the system and the fuzzy rules defined in Table 5, fuzzy collaborative control is performed. The following results are obtained: power and SOC curves for the two strategies when the fuel cell system ages by 2%, degradation rate curves for the fuel cell and power battery for the two strategies when the system ages by 2%, battery power and SOC curves for the two strategies when the fuel cell system ages by 4%, and degradation rate curves for the fuel cell and power battery for the two strategies when the system ages by 4%. Figure 14 , Figure 15 , Figure 16 as well as Figure 17 As shown.

[0261] The SOC curves reveal that the power and SOC trends after aging are largely consistent. When the fuel cell system ages by 2%, the fuzzy collaborative control strategy achieves a collaborative error of 0.00003%, reaching the collaborative target, while the ECMS strategy achieves a collaborative error of 0.0062%. Similarly, when the fuel cell system ages by 4%, the fuzzy collaborative control strategy achieves a collaborative error of 0.00008%, reaching the collaborative target, while the ECMS strategy achieves a collaborative error of 0.0048%. Comparing the dual-power-source degradation rate curves under different aging states using the ECMS strategy reveals that the ECMS collaborative error decreases with the aging of the fuel cell system. This is because aging leads to increased internal resistance and decreased capacity of the power battery. According to the power battery life degradation model, this will increase the degradation of the power battery, thereby reducing the collaborative error of the dual power sources.

[0262] The energy management method proposed in this invention, which considers the synergistic lifespan of dual power sources, can achieve synchronized degradation of both power sources within the optimal power range of fuel cells under different aging conditions. This effectively mitigates the risk of lifespan mismatch between the two power sources and significantly improves the system's economy and synergy. First, for the fuel cell, the impact of four operating conditions—start-stop, idling, variable load, and high power—on voltage decay was quantified. Simultaneously, for the power battery, the relationship between capacity decay rate and rate of change was quantified, and lifespan degradation models for both the fuel cell and the power battery were established. Second, the synergistic degradation characteristics of the dual power sources were analyzed, determining that the optimal power range is 8-19kW before aging and shrinks to 9-16kW at 4% aging. Subsequently, fuzzy rules were formulated, and a fuzzy control synergistic degradation energy management strategy adapted to aging characteristics was proposed. Finally, the effectiveness of the proposed strategy was verified under three states: no aging, 2% aging, and 4% aging. The results show that this strategy can constrain the synergistic error of the dual power sources to within 0.0001%, and compared with the multi-objective ECMS strategy, hydrogen consumption is reduced by 18%, and the synergistic rate is improved by at least 98.33%.

[0263] A system for implementing a dual-source lifetime coordinated energy management method for hybrid power systems includes a data acquisition module, a model building module, an aging state calculation module, a fuzzy rule adaptive module, and an energy management module.

[0264] The data acquisition module is used to collect vehicle speed, required power, SOC, voltage, and current in real time.

[0265] The model building module is used to build life degradation models for the entire vehicle powertrain system and dual power sources.

[0266] The aging state calculation module is used to calculate the aging degree of fuel cells and power batteries in real time, quantify the life decay state of dual power sources, and provide aging basis for fuzzy rule adaptive adjustment and energy management strategy optimization.

[0267] The fuzzy rule adaptive module is used to adaptively adjust the fuzzy variable domain, fuzzy rule base content, and membership function based on aging status, vehicle operating parameters, and dual power source collaborative error.

[0268] The energy management module is used to coordinate the power output of the fuel cell and the power battery according to control commands.

[0269] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section. The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the invention.

Claims

1. A dual-source lifetime coordinated energy management method for a hybrid power system, characterized in that, Includes the following steps: S1. Establishment of a dual-power-source hybrid power system model: including the construction of a hybrid power system model, the establishment of a fuel cell life degradation model, and the establishment of a power battery life degradation model; S2. Analysis of the synergistic degradation characteristics of dual power source lifetimes, including: S201. Construct a multi-objective equivalent fuel consumption minimization strategy ECMS; S202. Analysis of the synergistic degradation characteristics of dual power sources in the unaged state; S203. Analysis of the synergistic degradation characteristics of dual power source lifespan under aging conditions; S3. Proposal and verification of an energy management strategy considering the synergistic degradation of the lifespan of dual power sources.

2. The dual-source lifetime coordinated energy management method for a hybrid power system according to claim 1, characterized in that, The fuel cell life degradation model in S1 is specifically established as follows: Fuel cell performance degradation is closely related to start-stop, idling, load variation, and high-power operating conditions, as shown in the following mathematical expression: (1) In the formula, The cumulative voltage decay rate of the fuel cell over time t is the percentage decrease in voltage. To account for correction factors related to air pollution in actual use environments; , , and These represent the voltage attenuation ratios under start-up, idling, load changing, and high-power operating conditions at time t, respectively. , , and Calculated using the following formula: (2) In the formula, This is the start / stop indicator for the fuel cell, where 0 indicates shutdown and 1 indicates startup. For the output power of the fuel cell, This refers to the output power threshold of the fuel cell under idling conditions. This refers to the output power threshold of the fuel cell under high-power operating conditions. Represents a moment, n and n-1 represent two adjacent moments.

3. The dual-source lifetime coordinated energy management method for a hybrid power system according to claim 1, characterized in that, The power battery life degradation model in S1 is specifically established as follows: Model of capacity decay rate of power battery as follows: (3) In the formula, The charge / discharge rate of the power battery. The capacity decay rate at different rates; Ignoring the effects of storage on aging, a single charge-discharge test includes one charge and one discharge cycle, so the time required for one cycle of aging is: (4) In the formula, The time required for one cycle of aging. The time required for one charge or discharge cycle. The capacity is measured during capacity calibration before each cycle of aging. This refers to the charging and discharging current. The cycle capacity decay rate is converted to the time-dependent capacity decay rate using the following expression: The cyclic capacity decay rate in equation (3) is converted into the time-dependent capacity decay rate, as shown in the following expression: (5) In the formula, This represents the rate of degradation of the power battery per second. The battery's lifespan is considered to have reached its end when its capacity degrades by 20%. Assuming the nominal capacity of the lithium iron phosphate battery used is 20 Ah, then the percentage degradation per second of the battery is: ； In the formula, The percentage of battery degradation per second; Based on formulas (3) and (6), the suitable life degradation model for power batteries is as follows: 。 4. The dual-source lifetime coordinated energy management method for a hybrid power system according to claim 1, characterized in that, Specifically, S201 is as follows: The Equivalent Fuel Consumption Minimization Strategy (ECMS) is a local optimization strategy. Its key feature is its ability to dynamically correlate battery energy and hydrogen consumption through an equivalent conversion mechanism, achieving instantaneous optimization of the global problem. Its core idea is to use an equivalent factor to convert the energy absorption and release during battery charging and discharging into hydrogen consumption and compensation. In hybrid power system energy management, not only system economy but also durability must be considered. Therefore, the energy allocation problem of a fuel cell hybrid power system is transformed into: (8) In the formula, Represents the total equivalent hydrogen consumption. This represents the direct hydrogen consumption of fuel cells. Represents the equivalent hydrogen consumption of the power battery. The equivalent hydrogen consumption for fuel cell lifespan loss. The equivalent hydrogen consumption for the loss of power battery life; A compensation coefficient is introduced when calculating the equivalent hydrogen consumption of the power battery. The purpose is to ensure that the power battery operates within a reasonable range and to prevent overcharging or over-discharging; the expression for the compensation coefficient is: (9) In the formula, The equilibrium coefficient; SOC L SOC H These are the lower and upper limits of the SOC (State of Charge) of the power battery, respectively. Equivalent hydrogen consumption of power batteries The calculation method is as follows: (10) In the formula, Represents the power battery capacity. It is the average hydrogen consumption of the fuel cell. It is the average charging efficiency of the power battery; It is the average power of the fuel cell. It refers to the discharge efficiency of the power battery; It refers to the charging efficiency of the power battery; It is the average discharge efficiency of the power battery; During the operation of the power battery, the real-time charging and discharging efficiency is calculated using equation (11); (11) In the formula, This represents the internal resistance of the power battery during discharge. Represents the internal resistance of the power battery during charging; when When ≥0, it indicates that the power battery is in a discharging state; when When the value is less than 0, it indicates that the power battery is in a charging state. The square of the total open-circuit voltage of the battery pack; Calculated using equation (12): (12) In the formula, This represents the maximum output power of the fuel cell. The initial purchase cost of a 1 kW fuel cell, For the loss of fuel cells, The price of hydrogen, 10% is the end-of-life standard. This represents the proportion of the recyclable value of the fuel cell. Calculated using equation (13): (13) In the formula, The rated energy of the power battery, The initial purchase cost of a 1 kWh power battery. The loss rate of the power battery, This represents the proportion of the recyclable value of the power battery. The price of hydrogen is 20%, which is the end-of-life standard. Calculated by equation (14): (14) In the formula, and These represent the number of power batteries connected in series and in parallel, respectively. This refers to the rated voltage of the power battery. This refers to the rated capacity of the power battery. To ensure the proper functioning of the entire optimization system, the following constraints need to be added to the optimization problem, expressed as follows: (15) In the formula, , , These represent the power requirements of the entire vehicle, the power of the battery, and the power of the fuel cell system, respectively. and These represent the maximum and minimum output power of the power battery, respectively. This is the maximum output power of the fuel cell system. and These represent the minimum and maximum power variation ranges of the fuel cell system, respectively. This is the difference between the current fuel cell output power and the previous fuel cell output power.

5. The dual-source lifetime coordinated energy management method for a hybrid power system according to claim 1, characterized in that, Specifically, S202 is as follows: The characteristics of fuel cells under different power levels under multi-objective optimization were investigated, and the impact of fuel cell output power on overall hydrogen consumption was analyzed to determine the optimal output power in lifetime co-optimization. The specific optimization method is as follows: S2021. Calculate the degradation rate of fuel cells and power batteries respectively using the life degradation models of fuel cells and power batteries. S2022. Based on the lifespan degradation of the dual power sources in S2021 above, simulate and obtain the lifespan-coordinated vehicle speed point, coordinated hydrogen consumption, and coordinated error under different fuel cell output powers: Different fuel cell output powers were selected, and each output power was simulated independently. The simulation was run under comprehensive operating conditions, and the life degradation of the fuel cell and the power battery was accumulated in real time. When the ratio of life loss of the two met the preset coordination error threshold, the number of coordinated vehicle speed points consumed at this time was recorded. The total equivalent hydrogen consumption during the entire coordination process was calculated. The hydrogen consumption under different power levels was standardized to the same driving distance benchmark using the equivalence coefficient. The coordination error was calculated from the difference in the life loss ratio at the end of the simulation. The preset cooperative error threshold of 0.0001% is used as a constraint. ; Equivalent hydrogen consumption = Actual hydrogen consumption × Equivalent coefficient; Cooperative error = ; S2023. Considering the critical settings of fuel cells and power batteries under collaborative conditions; when the SOC of the power battery reaches 80%, the output power of the fuel cell is set to the critical value of 3.6 kW for idling conditions, and when the SOC drops to 40%, the output power of the fuel cell is set to the critical value of 21.7 kW for high-power conditions. Based on the above optimization method, the original constraint equation (15) is modified, and the optimized constraint is: (16) In the formula, This is due to the loss of the power battery; For losses in fuel cells; This is the cooperative error; By setting a collaborative error threshold, the output power of the power battery and the output power of the fuel cell under the collaborative degradation of the lifespan of the two power sources under multiple objectives are obtained; the power of the fuel cell includes the constant output power during the collaborative process and the critical idle power of the fuel cell to prevent overcharging of the power battery. The equivalent coefficients are obtained based on the ratio between the cooperative speed points at each power level and the maximum value of the cooperative speed points at all power levels. The number of constant power speed points is then equivalently calculated based on the maximum value. The calculation method for the equivalent coefficients is as follows: (17) In the formula, Equivalent coefficient; The number of coordinated vehicle speed points corresponding to different power levels; This represents the maximum number of coordinated vehicle speed points under different power levels.

6. The dual-source lifetime coordinated energy management method for a hybrid power system according to claim 1, characterized in that, Specifically, S203 is: S2031, Aging Characteristics Analysis of Fuel Cells and Power Batteries; Given the aging state of a fuel cell, the method for calculating the high-power critical point of a fuel cell is as follows: (18) In the formula, For fuel cells, the high-power criticality, The aging degree of the fuel cell; Battery aging also affects external characteristics, primarily manifested in capacity decay and increased internal resistance. Capacity decay curves and internal resistance curves were obtained through battery aging experiments. The capacity decay of the battery with the number of cycles during aging was transformed into the change in battery capacity over time (s). The calculation is as follows: (19) In the formula, Let i be the initial capacity of the power battery, and i be the simulation step size. Based on this, the aging rate of the battery Represented as: (20) Based on the aging state of the power battery, the internal resistance is calculated using equation (20): (21) In the formula, This refers to the internal resistance of the power battery when it is not yet aged. S2032. Analysis of the Co-degradation Characteristics of Dual Power Source Lifespan Considering Aging: In actual use, the performance of fuel cells and power batteries will degrade with increasing operating time. Considering the adjustment of the co-degradation of dual power source lifespan after actual multi-objective optimization, so that the degradation ratio of fuel cells and power batteries is always maintained at 1:2, the co-operation speed point is equivalent to the maximum value to analyze the impact of fuel cell output power on hydrogen consumption and dual power source lifespan under multi-objective optimization during system degradation. The relationship curve between hydrogen consumption and fuel cell power during the multi-objective coordination process during system degradation is obtained. Comparing the relationship curves of the non-aged state and the aged state, it can be seen that the optimal power output range also changes due to the change of the high-power critical point. Therefore, when formulating energy management strategies, it is necessary to dynamically adjust the optimal output power range of fuel cells according to the aging state.

7. The dual-source lifetime coordinated energy management method for a hybrid power system according to claim 1, characterized in that, Specifically, S3 is: S301. Determine the range of input and output variables and construct a language library and rule library, and then propose a dual-power source lifetime collaborative decay strategy based on fuzzy control. S302. Verify the dual-power source lifetime co-degradation strategy based on fuzzy control during both the unaged and aged periods.

8. The dual-source lifetime coordinated energy management method for a hybrid power system according to claim 7, characterized in that, Specifically, S301 is: The range of input and output variables is determined: The required power P of the whole vehicle re With the power battery SOC as an input variable, the fuel cell output power P fc P is the output variable. re The universe of discourse of is [-60, 70] kW, and the universe of discourse of SOC is [30, 100]%, P fc The domain of discourse is [0, 30] kW; Language library and rule base construction: The fuzzy subset of the vehicle's required power is set as {Very Low (RVL), Low (RL), Medium (RM), High (RH), Very High (RVH)}; the fuzzy subset of the power battery's SOC is set as {Very Low (SVL), Low (SL), Medium (SM), High (SH), Very High (SVH)}; and the fuzzy subset of the fuel cell's output power is set as {Very Low (FVL), Low (FL), Medium (FM), High (FH), Very High (FVH)}. The range of the demand power fuzzy subset setting is {[-30,-10], [-20,10], [5,30], [25,50], [45,70]}; the range of the power battery SOC fuzzy subset setting is {[30,45], [40,50], [47,58.9], [54,88], [85,100]}; the range of the fuel cell output power fuzzy subset setting is {[0,3.6], [3,13], [12,22], [20,26], [24,30]}; Secondly, triangular membership functions and trapezoidal membership functions are selected based on the characteristics of the input and output quantities, thereby constructing fuzzy rules to achieve the optimal power output of the fuel cell.

9. A system for implementing the dual-source lifetime coordinated energy management method for a hybrid power system according to any one of claims 1-8, characterized in that, It includes a data acquisition module, a model building module, an aging state calculation module, a fuzzy rule adaptive module, and an energy management module; The data acquisition module is used to collect vehicle speed, power demand, SOC, voltage, and current in real time. The model building module is used to build life degradation models for the entire vehicle powertrain system and dual power sources; The aging state calculation module is used to calculate the aging degree of fuel cells and power batteries in real time, quantify the life decay state of dual power sources, and provide aging basis for fuzzy rule adaptive adjustment and energy management strategy optimization. The fuzzy rule adaptive module is used to adaptively adjust the fuzzy variable domain, fuzzy rule base content, and membership function according to the aging state, vehicle execution parameters, and dual power source coordination error. The energy management module is used to coordinate the power output of the fuel cell and the power battery according to control commands.

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