EIS-based aging consistency assessment and energy management method for multi-stack fuel cell hybrid power system

Abstract

The present invention provides an EIS-based aging consistency assessment and energy management method for a multi-stack fuel cell hybrid system, which belongs to the field of fuel cell power system control technology. It includes: performing online electrochemical impedance detection on multiple stacks of a multi-stack fuel cell hybrid system to obtain electrochemical impedance spectrum data of each stack; then selecting the impedance value of the frequency band related to the aging characteristics of the stack, calculating the health index of each stack by a geometric method, and evaluating the aging consistency between the stacks based on it; finally, through the energy management strategy of a hierarchical finite state machine, according to the vehicle load demand, battery state of charge and the aging consistency assessment results between the stacks, dynamically optimize the power distribution between the sub-power systems and between the stacks, so that their output power matches their own working characteristics and aging degree. The present invention improves the aging consistency of multiple fuel cell stacks through closed-loop optimization control, improves system performance and extends the life of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cell power system control, and in particular to an EIS-based multi-stack fuel cell hybrid power system aging consistency assessment and energy management method. Background Art

[0002] Compared to single-stack fuel cell power systems, multi-stack fuel cell systems (MFCSs) can effectively improve power system power levels. By adding auxiliary power sources such as power batteries and supercapacitors, multi-stack fuel cell hybrid systems (MFCHSs) can be constructed, overcoming shortcomings of fuel cells such as poor dynamic performance and inability to recover energy, thereby better meeting the driving requirements of fuel cell commercial vehicles. Multi-stack fuel cell hybrid systems in a parallel configuration support independent control of the DC / DC converters, as well as fault elimination and inter-stack power allocation. These systems offer advantages such as enhanced reliability, system efficiency, and cost-effectiveness. However, in multi-stack fuel cell systems, inconsistent aging between stacks can seriously impact the system's power supply performance and service life. Due to complex operating conditions such as starting, stopping, idling, and acceleration / deceleration during vehicle operation, stack components such as proton exchange membranes and platinum catalysts are susceptible to aging. This can lead to uneven power distribution, uneven fuel supply, and temperature and humidity variations between stacks, resulting in inconsistent aging between stacks. This inconsistency not only limits the system's maximum output power, but also causes some fuel cell stacks to retire prematurely, thereby affecting the stability and durability of the entire multi-stack fuel cell system.

[0003] Currently, the methods for evaluating the degree of fuel cell aging mainly include polarization curve-based evaluation methods and electrochemical impedance spectroscopy-based evaluation methods. However, these methods have some limitations. The traditional polarization curve method is difficult to accurately reflect the aging state inside the fuel cell stack. Although the impedance spectroscopy method based on ECM parameter fitting can provide more in-depth information on the internal state of the fuel cell stack, it requires complex model construction and a large amount of computing resources, which is not suitable for real-time online evaluation. In addition, most existing energy management strategies are optimized around goals such as system economy, efficiency, and maximum power tracking, and rarely consider the negative impact of aging differences between multiple fuel cells on system performance.

[0004] Furthermore, traditional polarization curve-based aging assessment methods struggle to accurately reflect the internal aging state of fuel cell stacks and are unable to effectively assess the aging consistency across multiple fuel cell stacks. In multi-stack fuel cell systems, factors such as uneven power distribution, uneven fuel supply, and temperature / humidity differences lead to inconsistent aging across stacks, severely impacting the system's maximum output power and power supply performance. Existing multi-stack aging assessment methods lack consideration for the complex operating conditions of fuel cell commercial vehicles (such as start-stop, idling, acceleration, and deceleration), and are unable to fully reflect the aging of fuel cell stacks under actual operating conditions. Therefore, there is an urgent need for a method that can accurately assess the aging consistency of multiple fuel cell stacks to optimize energy management strategies and improve the system's power supply performance and service life. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide an EIS-based multi-stack fuel cell hybrid system aging consistency assessment and energy management method, which improves the aging consistency of fuel cell multi-stacks through closed-loop optimization control, improves system performance and extends the life of the power system. It does not require the construction of complex models, has a small amount of calculation, and is suitable for online applications of embedded controllers.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] An EIS-based aging consistency assessment method for a multi-stack fuel cell hybrid system includes the following steps:

[0008] S1. Performing electrochemical impedance spectroscopy (EIS) on multiple fuel cell stacks in a multi-stack fuel cell hybrid system to obtain electrochemical impedance spectroscopy data for each fuel cell stack.

[0009] S2. Based on the acquired electrochemical impedance spectroscopy data, select the impedance values ​​of the frequency bands related to the aging characteristics of the battery stack and calculate the health indicators of each battery stack using a geometric method;

[0010] S3. Based on the obtained health indicators, evaluate the aging consistency among the multiple fuel cell stacks to obtain an aging consistency evaluation result;

[0011] S4. Based on the obtained aging consistency evaluation results, the output power distribution of each power source is optimized through an energy stratification management strategy, thereby improving the aging consistency of multiple fuel cell stacks.

[0012] Preferably, in S1, the electrochemical impedance spectroscopy (EIS) includes two working modes, specifically:

[0013] Power distribution mode: Fuel cells and lithium batteries output power according to energy management strategies to meet load requirements;

[0014] EIS detection mode: Multiple fuel cells output a fixed power, and the lithium battery supplements the remaining power required by the load or absorbs excess power. In this mode, electrochemical impedance spectroscopy data is collected.

[0015] Preferably, in the EIS detection mode, a disturbance signal is applied to the DC / DC converter in the multi-stack fuel cell hybrid system to implement electrochemical impedance spectroscopy (EIS), specifically:

[0016] For the low frequency band of 1-100Hz, a voltage disturbance signal is superimposed on the set voltage reference of the droop control of the unidirectional DC / DC converter, so that the voltage loop input of the converter has an AC disturbance component;

[0017] For the high frequency band of 100-2000 Hz, a current disturbance signal is simultaneously superimposed on the input current reference of the unidirectional DC / DC converter, so that the current loop input of the converter has an AC disturbance component.

[0018] Preferably, the amplitude of the disturbance signal is controlled, specifically: a signal with a relatively large amplitude is selected from the AC components of the output current of the two fuel cell stacks as a reference signal, and the amplitude calculated in real time is used as a feedback value for closed-loop control of the disturbance signal amplitude, and the amplitudes of the AC components of the output current of the two fuel cell stacks do not exceed 10% of the DC output current of each.

[0019] Preferably, in S2, the health index includes HI1, and the calculation method of HI1 is: select the impedance points corresponding to 1 Hz and 100 Hz on the electrochemical impedance spectrum, and calculate the abscissa of the intersection of the perpendicular bisector of the line connecting the two points and the real axis as HI1, and the calculation formula is:

[0020] ;

[0021] ;

[0022] Where, HI1#A is the health index of the initial stack; HI1#B is the health index of the aging stack; Z reA-1Hz is the real part of the impedance of the initial stack at 1 Hz; Z reA-100Hz is the real part of the impedance of the initial stack at 100 Hz; Z imA-1Hz is the imaginary part of the impedance of the initial stack at 1 Hz; Z imA-100Hz is the imaginary impedance of the initial stack at 100 Hz; Z reB-1Hz is the real part of the impedance of the aged stack at 1 Hz; Z reB-100Hz is the real part of the impedance of the aged stack at 100Hz; Z imB-1Hz is the imaginary impedance part of the aged stack at 1 Hz; Z imB-100Hz is the imaginary part of the impedance of the aging stack at 100Hz.

[0023] Preferably, the health index further includes HI2, and the calculation method of HI2 is: select impedance points corresponding to 1Hz, 2Hz, 3Hz, 5Hz, 8Hz, 12Hz, 25Hz, 50Hz, and 100Hz on the electrochemical impedance spectrum, and calculate the average value of the length of the line connecting each impedance point and the reference point, which is the HI2. The calculation formula is:

[0024] ;

[0025] ;

[0026] Wherein, HI2#A is the health index of the initial stack calculated based on the geometric characteristics of the electrochemical impedance spectroscopy; HI2#B is the health index of the aged stack calculated based on the geometric characteristics of the electrochemical impedance spectroscopy; fre is the detection frequency of the impedance point, i.e., 1Hz, 2Hz, 3Hz, 5Hz, 8Hz, 12Hz, 25Hz, 50Hz, 100Hz; Z reA-fre is the real part of the impedance of the initial stack at the detection frequency of the impedance point, Z imA-fre is the imaginary impedance part of the initial stack at the detection frequency of the impedance point; Z reB-fre is the real part of the impedance of the aging stack at the detection frequency of the impedance point, Z imB-fre It is the imaginary part of the impedance of the aging stack at the detection frequency of the impedance point.

[0027] Preferably, in S3, the aging consistency among the plurality of fuel cells is evaluated by comparing the HI1 difference and HI2 difference of each fuel cell. A larger difference indicates a worse aging consistency among the plurality of fuel cells, and a smaller difference indicates a better aging consistency among the plurality of fuel cells.

[0028] The present invention also provides an energy management method for aging consistency of a multi-stack fuel cell hybrid system based on EIS, comprising the following steps:

[0029] Based on the evaluation results obtained by the EIS-based multi-stack fuel cell hybrid system aging consistency evaluation method, an energy management strategy is formulated through a hierarchical finite state machine; the energy management strategy formulated by the hierarchical finite state machine includes a global layer, a composite energy system layer, and a fuel cell multi-stack layer;

[0030] The power distribution mode and the EIS detection mode are switched according to the global layer. The power distribution mode is operated normally, and the EIS detection mode is switched to when the EIS detection is required.

[0031] The composite energy system layer optimizes the power distribution between the multi-stack power system and the auxiliary power source according to the charge state of the auxiliary power source and the load power demand;

[0032] The fuel cell multi-stack layer optimizes the output power distribution between each stack based on the aging consistency evaluation results and the output power required by the multi-stack power system, thereby improving the aging consistency of the multi-stack.

[0033] Preferably, in the EIS detection mode of the global layer, multiple fuel cells output fixed power, and the auxiliary power source supplements the remaining power required by the load or absorbs excess power; in the power distribution mode, power distribution is completed collaboratively by the composite energy system layer and the fuel cell multi-stack layer.

[0034] Preferably, the power distribution principle of the fuel cell multi-stack layer is: the power distribution coefficient is calculated based on the aging consistency evaluation result. λ ,based on λ The power distribution is divided into light-load range, medium-load range and heavy-load range according to the output power required by the multi-stack fuel cell hybrid system; in the light-load range, the fuel cell stack with a more serious aging degree outputs the minimum power in the linear working area, and the healthy fuel cell stack outputs a relatively large power; in the medium-load range, the healthy fuel cell stack outputs a large power, and the fuel cell stack with a more serious aging degree outputs a small power; in the heavy-load range, the healthy fuel cell stack outputs the maximum power in the linear working area, and the fuel cell stack with a more serious aging degree outputs a relatively small power; and each fuel cell stack works in the linear working area to improve the aging consistency between multiple fuel cell stacks.

[0035] By incorporating online electrochemical impedance spectroscopy (EIS) detection technology, the present invention overcomes the shortcomings of traditional polarization curve-based assessment methods, which struggle to accurately reflect the internal aging state of a fuel cell stack. This method can more accurately assess the aging consistency across multiple fuel cells, effectively improving the accuracy and reliability of the assessment. Furthermore, the use of a characteristic impedance-based assessment method avoids the complex model construction and computational resource requirements of ECM parameter fitting, reducing system complexity and making the assessment method simpler and more practical, making it suitable for online assessment applications. The present invention also considers the aging of a multi-stack system under complex operating conditions such as start-stop, idling, acceleration, and deceleration. Using EIS online detection technology, the system comprehensively reflects the aging state of the fuel cell stack under actual operating conditions, improving the comprehensiveness and accuracy of the assessment. By optimizing the energy management strategy and effectively adjusting for aging differences between fuel cells, the system's maximum output power is increased, power supply performance is significantly improved, and the overall service life of the power system is extended. Furthermore, by adopting a parallel operation structure for multiple power sources, the intelligent control of the DC / DC converter enables flexible power distribution among multiple fuel cells, effectively addressing issues such as uneven power distribution, uneven fuel supply, and temperature / humidity variations between fuel cells, significantly improving the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1This is a flow chart of an EIS-based aging consistency assessment method for a multi-stack fuel cell hybrid system of the present invention;

[0037] Figure 2 A schematic diagram of a multi-stack fuel cell hybrid power system for a fuel cell commercial vehicle provided by the present invention;

[0038] Figure 3 This is the control block diagram after the optimization of the online EIS detection strategy for a single stack and single converter provided by the present invention;

[0039] Figure 4 The impedance spectrum data diagram of the fuel cell aging process measured by the electrochemical workstation provided by the present invention;

[0040] Figure 5 The impedance spectrum curve obtained by fitting the electrochemical workstation impedance spectrum curve and the ECM impedance spectrum curve provided by the present invention;

[0041] Figure 6 A diagram illustrating the HI1 calculation method based on the geometric characteristics of electrochemical impedance spectroscopy provided by the present invention;

[0042] Figure 7 A diagram illustrating the HI2 calculation method based on the geometric characteristics of electrochemical impedance spectroscopy provided by the present invention;

[0043] Figure 8 A structural diagram of the energy management strategy based on a hierarchical finite state machine provided by the present invention;

[0044] Figure 9 The power distribution flow chart of the composite energy system provided by the present invention; wherein, Figure 9 (a) is the branch diagram of the power allocation process when the SOC is less than 40%. Figure 9 (b) is a branch diagram of the power allocation process when the SOC < 40% condition is not met but the SOC < 80% condition is met. Figure 9 (c) is the branch diagram of the power allocation process when the SOC < 80% condition is not met;

[0045] Figure 10 A flow chart showing the power distribution flow for multiple fuel cell stack layers provided by the present invention;

[0046] Figure 11 This is a structural block diagram of the HIL simulation platform provided in Example 1 of the present invention;

[0047] Figure 12 This is a HIL simulation waveform diagram under low SOC range working conditions provided by Example 1 of the present invention; wherein, Figure 12 (a) is the HIL simulation waveform of the bus voltage under low SOC range conditions. Figure 12(b) is the HIL simulation waveform of the fuel cell output power and load power under low SOC range conditions. Figure 12 (c) is the HIL simulation waveform of the lithium battery SOC under low SOC range conditions;

[0048] Figure 13 This is a HIL simulation waveform diagram under high SOC range working conditions provided by Example 1 of the present invention; wherein, Figure 13 (a) is the HIL simulation waveform of the bus voltage under high SOC range conditions. Figure 13 (b) is the HIL simulation waveform of the fuel cell output power and load power under high SOC range conditions. Figure 13 (c) is the HIL simulation waveform of the lithium battery SOC under high SOC range conditions;

[0049] Figure 14 This is a HIL simulation waveform diagram under the medium SOC range working condition provided by Example 1 of the present invention; wherein, Figure 14 (a) is the HIL simulation waveform of the bus voltage under the medium SOC range condition. Figure 14 (b) is the HIL simulation waveform of the fuel cell output power under the medium SOC range. Figure 14 (c) is the HIL simulation waveform of the load power under the medium SOC range;

[0050] Figure 15 HIL simulation waveform diagram of the output power change rate of each power source under the first and third operating conditions provided in Example 1 of the present invention;

[0051] Figure 16 Statistical results of the output power change rate of each power source under the first and third operating conditions provided in Example 1 of the present invention;

[0052] Figure 17 This is a current waveform diagram of FC#A and FC#B under the second operating condition provided by Example 1 of the present invention;

[0053] Figure 18 This is a voltage waveform diagram of FC#A and FC#B under the second working condition provided by Example 1 of the present invention;

[0054] Figure 19 HIL simulation waveforms of the output power of FC#A and FC#B under the first and third operating conditions provided in Example 1 of the present invention;

[0055] Figure 20 This is a statistical diagram of the output power of each power source under the first and third operating conditions provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0056] like Figure 1 As shown, the present invention provides an EIS-based aging consistency assessment method for a multi-stack fuel cell hybrid system, comprising the following steps:

[0057] S1. Performing electrochemical impedance spectroscopy (EIS) on multiple fuel cell stacks in a multi-stack fuel cell hybrid system to obtain electrochemical impedance spectroscopy data for each fuel cell stack.

[0058] S2. Based on the acquired electrochemical impedance spectroscopy data, select the impedance values ​​of the frequency bands related to the aging characteristics of the battery stack and calculate the health indicators of each battery stack using a geometric method;

[0059] S3. Based on the obtained health indicators, evaluate the aging consistency among the multiple fuel cell stacks to obtain an aging consistency evaluation result;

[0060] S4. Based on the obtained aging consistency evaluation results, the output power distribution of each power source is optimized through an energy stratification management strategy, thereby improving the aging consistency of multiple fuel cell stacks.

[0061] The above method is mainly used to study the aging inconsistency of multiple fuel cell stacks in the multi-stack fuel cell hybrid system of fuel cell commercial vehicles, and it is necessary to analyze the composition of the multi-stack fuel cell hybrid system. Figure 2 As shown, the power system of a fuel cell commercial vehicle mainly consists of a multi-stack fuel cell hybrid system and an electric drive system.

[0062] A multi-stack fuel cell hybrid system typically consists of a multi-stack fuel cell power system (MFCS) and an auxiliary power system (APS). The MFCS is the primary power system, utilizing multiple modular fuel cell stacks of the same model as the power source to provide continuous and stable power to the electric drive system. The APS is the auxiliary power system, typically utilizing lithium batteries as the power source to compensate for the high dynamic power requirements of the electric drive system and absorb feedback energy generated during vehicle braking. Due to the low output voltage level of the on-board fuel cell, a unidirectional DC / DC boost converter is required in the downstream stage to meet the DC bus voltage level requirements. The on-board lithium battery is connected to a bidirectional DC / DC converter to meet the DC bus voltage level requirements and charge / discharge control requirements.

[0063] Multi-stack fuel cell hybrid systems usually adopt a parallel structure, in which each power source is connected to the DC bus through an independent DC / DC boost converter. This has advantages in system reliability, redundancy, and flexibility of control strategies, and can better meet the driving needs of fuel cell commercial vehicles.

[0064] Specifically, in S1, the electrochemical impedance spectroscopy (EIS) includes two working modes. The control strategy of the multi-stack EIS online detection method is as follows: Figure 3 As shown, specifically:

[0065] Power distribution mode: Fuel cells and lithium batteries output power according to energy management strategies to meet load requirements;

[0066] EIS detection mode: Multiple fuel cells output a fixed power, and the lithium battery supplements the remaining power required by the load or absorbs excess power. In this mode, electrochemical impedance spectroscopy data is collected.

[0067] In a multi-stack fuel cell hybrid system, the unidirectional DC / DC converter adopts voltage and current dual closed-loop control, and the droop control based on virtual resistance will be used as the power outer loop, which is set outside the voltage and current dual closed-loop control. Therefore, for the low frequency band 1-100Hz, it is necessary to set the voltage in the droop control. V set The reference voltage disturbance signal is superimposed V per , so that the voltage loop input of the two converters has an AC disturbance component; for the high frequency band 100-2000 Hz, it is necessary to I inA 、 I inB The current disturbance signal is superimposed at the reference point I pert , resulting in AC disturbance components in the current loop inputs of both converters. To manage the charge and discharge power of the lithium battery, the bidirectional DC / DC converter adopts a current loop control strategy based on inductor current feedback. Compared to the dual closed-loop control of voltage outer loop and current inner loop, single current loop control has a wider bandwidth, giving the APS better dynamic response to load fluctuations.

[0068] In order to improve the accuracy of online multi-stack EIS detection, it is necessary to increase the signal-to-noise ratio of the voltage and current sampling of the stack. Under the premise of ensuring the effectiveness of the detection, the amplitude of the AC component of the output current of the two stacks should be increased as much as possible. and , thereby increasing the amplitude of the AC component of the output voltage and .

[0069] Therefore, the signal with the larger amplitude is used as the reference signal, and the amplitude calculated in real time is used as the feedback value to adjust the amplitude of the disturbance signal. EIS amp In the closed loop control, thus ensuring and Do not exceed the DC output current of the battery stack and 10% of.

[0070] A 40-hour accelerated aging test was conducted using the FCS-C1000. By configuring an electronic load, the fuel cell's output current waveform was set to a triangular wave to simulate the operating conditions of a PEMFC connected to a DC / DC converter. The triangular wave current had a DC component of 35A, a current ripple of ±3.5A, a frequency of 20kHz, and a duty cycle of 0.5.

[0071] During the aging test, the current amplifier GAMRY Reference 30K BOOSTER was used in conjunction with the electrochemical workstation GAMRY Reference 3000 to perform EIS testing on the FCS-C1000 to obtain EIS data inside the stack under different aging conditions. The EIS detection frequency range covers 0.05Hz to 10kHz, and the test was performed every five hours at the steady-state operating point. The impedance curve obtained is shown in the figure below. Figure 4 As shown, the electrochemical impedance spectrum has an obvious outward expansion trend in the medium and low frequency range, especially when comparing the impedance curves at the 5th hour and the 40th hour, this trend is particularly obvious. Therefore, these two impedance spectrum curves are selected to characterize the impedance characteristics of the initial fuel cell stack and the aged fuel cell stack, respectively, for subsequent comparative analysis. The initial fuel cell stack and the aged fuel cell stack are represented as FC#A and FC#B, respectively. Taking into account that the impedance spectrum data of extremely high frequency and extremely low frequency are difficult to clearly reflect the aging degree of the fuel cell, the impedance data of FC#A and FC#B are fitted with the least squares method in the frequency range of 1~2000Hz to obtain the ECM parameters; at the same time, in order to verify that the ECM parameters obtained based on the EIS data fitting can well restore the internal impedance characteristics of the fuel cell, the impedance spectrum curve is drawn based on the ECM parameters and compared with the measured impedance spectrum curve. The results are shown as follows. Figure 5 As shown, the red curve represents the measured impedance spectrum curve of FC#A and FC#B, and the blue curve represents the impedance spectrum curve based on ECM and fitting parameters.

[0072] Furthermore, the present invention selects characteristic impedance values ​​at 9 frequencies within the range of 1 to 100 Hz to calculate the HI of the stack. In order to clearly describe the multi-stack aging consistency evaluation method based on EIS geometric characteristics proposed in this paper, combined with Figure 5 The impedance curve in the paper is used to analyze and explain the proposed HI calculation method. Figure 6 , select the impedance points corresponding to 1Hz and 100Hz on the electrochemical impedance spectrum, and calculate the horizontal coordinate of the intersection of the perpendicular bisector of the line connecting the two points and the real axis, which is HI1. The calculation formula is:

[0073] ;

[0074] ;

[0075] Where, HI1#A is the health index of the initial stack; HI1#B is the health index of the aging stack; Z reA-1Hz is the real part of the impedance of the initial stack at 1 Hz; Z reA-100Hz is the real part of the impedance of the initial stack at 100 Hz; Z imA-1Hz is the imaginary part of the impedance of the initial stack at 1 Hz; Z imA-100Hz is the imaginary impedance of the initial stack at 100 Hz; Z reB-1Hz is the real part of the impedance of the aged stack at 1 Hz; Z reB-100Hz is the real part of the impedance of the aged stack at 100Hz; Z imB-1Hz is the imaginary impedance part of the aged stack at 1 Hz; Z imB-100Hz is the imaginary part of the impedance of the aging stack at 100Hz.

[0076] Reference Figure 7 The health index also includes HI2. The calculation method of HI2 is as follows: select impedance points corresponding to 1Hz, 2Hz, 3Hz, 5Hz, 8Hz, 12Hz, 25Hz, 50Hz, and 100Hz on the electrochemical impedance spectrum, and calculate the average length of the line connecting each impedance point and the reference point, which is the HI2. The calculation formula is:

[0077] ;

[0078] ;

[0079] Wherein, HI2#A is the health index of the initial stack calculated based on the geometric characteristics of the electrochemical impedance spectroscopy; HI2#B is the health index of the aged stack calculated based on the geometric characteristics of the electrochemical impedance spectroscopy; fre is the detection frequency of the impedance point, i.e., 1Hz, 2Hz, 3Hz, 5Hz, 8Hz, 12Hz, 25Hz, 50Hz, 100Hz; Z reA-fre is the real part of the impedance of the initial stack at the detection frequency of the impedance point, Z imA-fre is the imaginary impedance part of the initial stack at the detection frequency of the impedance point; Z reB-fre is the real part of the impedance of the aging stack at the detection frequency of the impedance point, Z imB-fre It is the imaginary part of the impedance of the aging stack at the detection frequency of the impedance point.

[0080] In summary, HI1 reflects the overall rightward shift of the low-frequency impedance curve, primarily reflecting the increasing trend of the stack's ohmic resistance; HI2 reflects the outward diffusion of the low-frequency impedance curve, primarily reflecting the increasing trend of the stack's mass transfer impedance. Together, HI1 and HI2 reflect the geometric change trend of the impedance spectrum curve during stack aging, enabling assessment of the current stack aging level and conversion of EIS data obtained through online testing into internal stack aging assessment parameters, providing a basis for subsequent energy management strategies.

[0081] This HI parameter calculation method is based on data from a 40-hour accelerated aging test of the FCS-C1000 fuel cell. The proposed HI parameter is closely related to the geometric characteristics of the impedance spectrum. For other fuel cell models, whose internal impedance characteristics and aging characteristics vary, preliminary testing can be conducted to improve the proposed HI parameter calculation method to better adapt it to different fuel cell stack models.

[0082] Furthermore, in S3, the aging consistency among the plurality of fuel cells is evaluated by comparing the HI1 difference and HI2 difference of each fuel cell. A larger difference indicates a worse aging consistency among the plurality of fuel cells, and a smaller difference indicates a better aging consistency among the plurality of fuel cells.

[0083] Reference Figure 8 The present invention also provides an energy management method for aging consistency of a multi-stack fuel cell hybrid system based on EIS, comprising the following steps:

[0084] Based on the evaluation results obtained by the EIS-based multi-stack fuel cell hybrid system aging consistency evaluation method, an energy management strategy is formulated through a hierarchical finite state machine; the energy management strategy formulated by the hierarchical finite state machine includes a global layer, a composite energy system layer, and a fuel cell multi-stack layer;

[0085] In order to integrate the multi-stack EIS online detection method into the EMS and realize the closed loop of "impedance detection-aging consistency assessment-power optimization distribution-output power regulation", the global layer is divided into two corresponding intervals according to the two working modes of the multi-stack EIS online detection method, namely the power distribution mode and the EIS detection mode.

[0086] The system normally operates in power-distribution mode, which optimizes the output power distribution of each power source in the hybrid system. This power-distribution strategy is complex and requires subdivision into the composite energy system layer and the fuel cell multi-stack layer. Based on EIS testing requirements, the system operates in EIS detection mode, which features a simpler power-distribution strategy: the fuel cell operates at a fixed operating point, while the lithium battery supplements the load's power or absorbs excess power.

[0087] The hybrid energy system layer optimizes the power distribution between the MFCS and APS based on the lithium battery SOC (State of Charge) and the power required by the current load, avoiding overcharging / discharging of the lithium battery, reducing the output power variation rate of the fuel cell, and extending the service life of the power source.

[0088] The fuel cell multi-stack layer optimizes the output power distribution among multiple stacks in the MFCS based on the multi-stack aging consistency assessment results and the output power required by the MFCS, thereby improving the aging consistency among the multiple stacks and extending the overall life of the MFCS system.

[0089] The power allocation strategy at the global layer is relatively simple and will not be described in detail here; however, the power allocation strategy at the composite energy system layer and the fuel cell multi-stack layer is relatively complex and will be described and analyzed in detail below.

[0090] Reference Figure 9 In (a), (b) and (c), the power allocation strategy of the hybrid energy system is based on the lithium battery SOC and the current load power required to optimize the power allocation between the MFCS and the APS. It is necessary to achieve three main goals: (1) Manage the lithium battery SOC to avoid overcharging / discharging of the lithium battery, extend the service life of the lithium battery, and ensure that when the global layer switches to the EIS detection mode, the lithium battery has sufficient SOC margin to supplement or absorb the load power demand and support multi-stack EIS online detection. (2) When the load power is less than the minimum output power of the MFCS, the lithium battery absorbs the excess power; when the load power is greater than the maximum output power of the MFCS, the lithium battery supplements the insufficient power. (3) The power required by the load is divided into frequencies, and the fuel cell outputs a continuous and stable low-frequency power; the lithium battery compensates for the high dynamic power demand of the load.

[0091] In order to achieve the above goals, it is necessary to calculate the power consumption of the lithium battery according to the state of charge (SOC) and the current load power demand. P load , the composite energy system layer is divided into three intervals with a total of eleven sub-modes.

[0092] According to the state of charge (SOC) of the lithium battery, the composite energy system layer is divided into three intervals: low SOC interval, medium SOC interval and high SOC interval, as follows: (1) The low SOC interval (SOC < 40%) is a non-ideal interval. The lithium battery SOC is too low and there is a risk of over-discharge. The lithium battery needs to be charged to make the SOC enter the medium SOC interval. Therefore, it is necessary to appropriately increase the output power of the MFCS to charge the lithium battery while meeting the load power demand. (2) The medium SOC interval (40% < SOC < 80%) is an ideal interval. The lithium battery SOC has sufficient margin. When the global layer switches to the EIS detection mode, the lithium battery can supplement or absorb the load power demand. Therefore, there is no need to increase or decrease the output power of the MFCS. (3) The high SOC interval (SOC > 80%) is a non-ideal interval. The lithium battery SOC is too high and there is a risk of over-charge. The lithium battery needs to be discharged to make the SOC enter the medium SOC interval. Therefore, it is necessary to appropriately reduce the output power of the MFCS so that the lithium battery can output more power to meet the load power demand.

[0093] Based on the division of the above three SOC intervals, according to the current vehicle power demand P load , MFCS maximum output power P MFCSmax and minimum output power P MFCSmin , optimal charge / discharge power for lithium batteries P bat,opt The numerical relationship between them can be further divided into eleven sub-modes to optimize the power distribution of MFCS and APS. Among them, the linear working area of ​​a single stack is 500~1050 W, so P MFCSmax =2100W, P MFCSmin =1000W, P bat,opt =300W.

[0094] In order to avoid frequent changes in the output power of the fuel cell, no matter which range the lithium battery SOC is in, the lithium battery will bear the high-frequency power, which will have the effect of "peak shaving and valley filling" on the load fluctuation. P load Determine the reference value of MFCS output power P MFCS,set The power difference between the two is the power reference value of APS P APS,set , the formula is:

[0095] ;

[0096] The obtained power allocation flow chart of the composite energy system layer is as follows: Figure 9 As shown, each sub-mode is specifically described and analyzed below in conjunction with the flow chart.

[0097] The eleven sub-modes of the composite energy system layer are as follows:

[0098] (1) Four sub-modes included in the low SOC range

[0099] Mode 1: SOC<40%, ;

[0100] The power of lithium battery is low, the power demand of vehicle is small, and it is far less than the minimum output power of MFCS. In order to prevent the charging power of lithium battery from being too high, MFCS outputs minimum power to reduce the power absorbed by lithium battery. , APS absorbed power , SOC increases.

[0101] Mode 2: SOC<40%, ;

[0102] The power level of the lithium battery is low, and the vehicle power demand is close to the output power range of the MFCS. In order to steadily improve the SOC of the lithium battery, the output power of the MFCS is appropriately increased, and the excess power is maintained at the optimal charging power of the lithium battery. Therefore, the output power of the MFCS , APS absorbed power , SOC increases.

[0103] Mode 3: SOC<40%, ;

[0104] The power of the lithium battery is low, and the vehicle power demand is large, which is close to the maximum output power of the MFCS. In order to improve the SOC of the lithium battery, the MFCS outputs the maximum power, and the excess power is absorbed by the lithium battery. Therefore, the MFCS output power , APS absorbed power , SOC increases.

[0105] Mode 4: SOC<40%, ;

[0106] The power of the lithium battery is low, and the vehicle power demand is large, which is greater than the maximum output power of the MFCS. In order to meet the power required by the load, the MFCS is made to output the maximum power, and the insufficient power is supplemented by the lithium battery. Therefore, the output power of the MFCS is , APS output power , SOC decreases.

[0107] (2) The three sub-modes included in the SOC interval

[0108] Mode 5: 40%<SOC<80%, ;

[0109] The power of the lithium battery is moderate, the vehicle power demand is small, and is less than the minimum output power of the MFCS. In order to meet the power required by the load, the MFCS is made to output the minimum power, and the excess power is supplemented by the lithium battery. Therefore, the MFCS output power , APS absorbed power , SOC increases.

[0110] Mode 6: 40%<SOC<80%, ;

[0111] The lithium battery has a moderate charge and the vehicle power demand is moderate, which is within the output power range of the MFCS. In order to keep the lithium battery's SOC moderate and allow the MFCS to output the power required by the load, the lithium battery does not need to output or absorb power. Therefore, the MFCS output power , APS power is zero and SOC remains unchanged.

[0112] Mode 7: 40%<SOC<80%, ;

[0113] The lithium battery has a moderate power level, and the vehicle's power demand is large, which is greater than the maximum output power of the MFCS. In order to meet the power required by the load, the MFCS is made to output the maximum power, and the insufficient power is supplemented by the lithium battery. Therefore, the MFCS output power , APS output power , SOC decreases.

[0114] (3) Four sub-modes included in the high SOC range

[0115] Mode 8: SOC>80%, ;

[0116] The lithium battery has a high power level, and the vehicle power demand is small, which is less than the minimum output power of the MFCS. In order to meet the power required by the load, the MFCS is made to output the minimum power, and the excess power is supplemented by the lithium battery. Therefore, the MFCS output power , APS absorbed power , SOC increases.

[0117] Mode 9: SOC>80%, ;

[0118] The lithium battery has a high power level, the vehicle power demand is small, and is close to the minimum output power of the MFCS. In order to reduce the SOC of the lithium battery, the MFCS is made to output the minimum power, and the insufficient power is absorbed by the lithium battery. Therefore, the MFCS output power , APS output power , SOC decreases.

[0119] Mode 10: SOC>80%,

[0120] The power level of the lithium battery is high, and the vehicle power demand is close to the output power range of the MFCS. In order to steadily reduce the SOC of the lithium battery, the output power of the MFCS is appropriately reduced, and the insufficient power is maintained at the optimal discharge power of the lithium battery. Therefore, the output power of the MFCS , APS output power , SOC decreases.

[0121] Mode 11: SOC>80%, ;

[0122] The power of lithium battery is high, and the power demand of vehicle is high, which is much greater than the maximum output power of MFCS. In order to prevent the lithium battery from discharging too much power, MFCS is set to output the maximum power and reduce the output power of lithium battery. , APS output power , SOC decreases.

[0123] Based on the above, the proposed hybrid energy system-level power allocation strategy optimizes power distribution between the MFCS and APS, preventing overcharging / discharging of the lithium-ion battery and extending its service life. By allowing the lithium-ion battery to bear high-frequency load fluctuations, the MFCS output power variation rate can be reduced, extending the service life of the fuel cell stack. Maintaining the lithium-ion battery SOC within a moderate range provides sufficient SOC margin for online EIS testing of multiple fuel cell hybrid systems.

[0124] In order to achieve power balance between fuel cells and improve the aging consistency of multiple fuel cells, it is necessary to evaluate the aging consistency between multiple fuel cells based on the HI and the output power required by the MFCS. , optimize the distribution of output power of multiple sub-stacks within the MFCS.

[0125] The basic idea of ​​the power allocation strategy for multiple fuel cell stacks is to reduce the output power of stacks with more severe aging and increase the output power of stacks with better health, so as to achieve power balance between stacks, thereby improving the aging consistency between multiple stacks and extending the overall life of the MFCS system.

[0126] Based on the proposed stack health index HI, the power allocation coefficient considering the aging consistency of multiple stacks is calculated λ , the formula is:

[0127] ;

[0128] The power of multiple sub-stacks in the MFCS is optimally distributed. The output power of each sub-stack is calculated as follows: ;

[0129] The fuel cell multi-stack layer needs to ensure that each stack operates within the linear operating range of 500~1050W to prevent the output power of a severely aged stack from being less than 500W and the output power of a healthy stack from being greater than 1050W. The formula is: ;

[0130] Calculate the applicable power range:

[0131] ;

[0132] Calculated from this λ ≤0.177, which means that under the premise of ensuring that the fuel cell stack operates in the linear working area, the greater the power difference between the fuel cells, the smaller the allowable working range of the fuel cell stack.

[0133] Among them, and To divide the nodes, the fuel cell multi-stack layer can be divided into three intervals: light load interval, medium load interval and heavy load interval. The resulting fuel cell multi-stack power distribution flow chart is as follows: Figure 10 As shown, the three power ranges of the fuel cell multi-stack layer are as follows:

[0134] (1) Light load range, ; In this range Small, FC#B outputs the minimum power in the linear working area, and FC#A outputs a relatively large power. The calculation formula is as follows:

[0135] ;

[0136] (2) Medium load interval, ; In this range Moderate, FC#A outputs a larger power, FC#B outputs a smaller power. The calculation formula is as follows: ;

[0137] (3) Heavy load interval, ; In this range FC#A outputs the maximum power in the linear operating area, and FC#B outputs a relatively small power. The calculation formula is as follows:

[0138] ;

[0139] Based on the above content, the proposed fuel cell multi-stack power allocation strategy is used to optimize the output power of multiple sub-stacks in the MFCS, so that the stacks with more serious aging conditions output lower power, and the stacks with better health conditions output higher power. This can achieve power balance between the stacks, improve the aging consistency between the stacks, and extend the overall life of the power system.

[0140] Example 1

[0141] This embodiment is based on two Typhoon HIL 404s to build a fuel cell multi-stack aging consistency hardware-in-the-loop real-time simulation (HIL) platform and conduct simulation verification. The HIL simulation platform structure diagram is as follows: Figure 11 As shown, Platform-1 primarily includes fuel cell models, lithium battery models, and DC / DC converter models. These models are all built within an FPGA (field programmable gate array) core to meet the requirements of high switching frequency, model accuracy, real-time solution performance, and I / O sampling rate, with a simulation step size of 500ns. Platform-2 primarily includes the proposed energy management strategy algorithm that considers multi-stack aging consistency. These algorithms run within the ARM (Advanced Reduced Instruction Set Machine) core of a real-time processor, with a simulation step size of 10μs. Platform-1 and Platform-2 are controlled by host computers-1 and -2, respectively. Signals between the platforms are exchanged via the high-speed I / O ports of the Typhoon HIL 404. The human-computer interaction interface is designed and implemented based on the Typhoon HIL Control Center software. In this example, the Argonne National Laboratory Toyota Mirai test data, No. 61712038, was selected as the test condition. In the simulation model, the stack parameters are FC#A and FC#B, as shown in Table 1.

[0142] Table 1 FCS-C1000 ECM parameters obtained based on EIS data fitting

[0143] ;

[0144] Furthermore, hardware-in-the-loop real-time simulation tests are conducted under different SOC range conditions, including:

[0145] (1) HIL simulation verification and analysis in low SOC range conditions

[0146] In order to verify the feasibility and effectiveness of the designed energy management strategy in the low SOC range, the initial SOC of the lithium battery is 35%, and the power distribution coefficient λ is zero, the simulation step is 5e-7 seconds, the total simulation time is 180 seconds, and the HIL simulation results are as follows Figure 12 As shown. Figure 12 In (a), (b), and (c), when the load power changes, the hybrid system adjusts according to the designed energy management system (EMS) to meet the load power demand and maintain a stable DC bus voltage. The lithium battery has a faster dynamic response. When the load suddenly changes, the EMS controls the lithium battery to buffer high-frequency power fluctuations, reducing the output power variation rate of the fuel cell. This verifies that the EMS's frequency division of load power is beneficial for extending the fuel cell's service life. When the SOC is too low, the EMS controls the MFCS to output more power, increasing the excess power that the lithium battery needs to absorb. The lithium battery SOC increases by 1.5%, avoiding over-discharge, and verifies that the EMS can effectively regulate the lithium battery SOC under low SOC conditions and extend the battery life. The above HIL simulation results verify the effectiveness and feasibility of the EMS in the low SOC range.

[0147] (2) Simulation verification and analysis in high SOC range

[0148] In order to verify the feasibility and effectiveness of the designed energy management strategy in high SOC range, the initial SOC of the lithium battery is 85%, and the power distribution coefficient λ is zero, the simulation step is 5e-7 seconds, the total simulation time is 120 seconds, and the HIL simulation results are as follows Figure 13 As shown. Figure 13 As shown in (a), (b), and (c), when the load power changes, the hybrid system adjusts according to the designed EMS to meet the load power demand and maintain a stable DC bus voltage. The lithium battery has a faster dynamic response. When the load suddenly changes, the EMS controls the lithium battery to buffer high-frequency power fluctuations, reducing the output power variation rate of the fuel cell. This verifies that the EMS's frequency division of load power is beneficial for extending the fuel cell's service life. When the SOC is too high, the EMS controls the MFCS to output less power, increasing the power required to replenish the lithium battery. The lithium battery SOC is reduced by 0.3%, avoiding overcharging, and verifying that the EMS can effectively regulate the lithium battery SOC under high SOC conditions and extend the battery life. The above HIL simulation results verify the effectiveness and feasibility of the EMS in the high SOC range.

[0149] (3) Simulation verification and analysis in the medium SOC range

[0150] In order to verify the feasibility and effectiveness of the designed energy management strategy in the medium SOC range, the 120-second operating condition is cycled three times. The system will run in power distribution mode, transition mode and EIS detection mode respectively, and conduct a complete closed-loop HIL simulation verification of "impedance detection-aging consistency assessment-power optimization distribution-output power regulation". The initial SOC of the lithium battery is 50%, and the power distribution coefficient is λThe initial value is zero, the simulation step is 5e-7 seconds, and the total simulation time is 360 seconds. The HIL simulation results are as follows: Figures 14 to 20 shown.

[0151] In order to verify the feasibility and effectiveness of the two working modes in the global layer, it is necessary to analyze the bus voltage, output power of each power source and load power waveform of the whole process, such as Figure 14 As shown in (a), (b), and (c) in Figure 1 . The system switches between power distribution mode, transition mode, and EIS detection mode, maintaining a stable bus voltage and ensuring that the hybrid system meets the load power requirements. In the first operating condition, the initial value of the power distribution coefficient λ is zero. In the second operating condition, three multi-stack EIS online tests, multi-stack aging consistency assessments, and calculation of the power distribution coefficient λ are completed. In the third operating condition, the power distribution coefficient λ is updated to 12.72%. These HIL simulation results verify that the designed EMS can operate stably under medium SOC conditions and that the mode switching designed at the global level does not affect the normal operation of the system.

[0152] In order to verify the effect of load power frequency division by the composite energy system layer in EMS, it is necessary to conduct statistics and analysis on the output power change rate of each power source under the power distribution mode, such as Figure 15 As shown in Figure 2, when the load increases or decreases, the lithium battery mainly bears the high-frequency load fluctuations, and the fuel cell bears the low-frequency load fluctuations. The statistical results of the output power change rate of each power source are shown in Figure 2. Figure 16 As shown, in the first operating condition, 74.1% of FC#A and FC#B output power variation rates were less than ±50 W / s, while 48.2% of lithium battery output power variation rates exceeded ±50 W / s. In the third operating condition, 77.6% of FC#A and 76.6% of FC#B output power variation rates were less than ±50 W / s, while 46.3% of lithium battery output power variation rates exceeded ±50 W / s. These HIL simulation results verify that the EMS's hybrid energy system layer effectively allocates low-frequency power to the MFCS and high-frequency power to the APS, which helps reduce the aging of fuel cells.

[0153] In order to analyze the detection effect of the EIS detection mode in the global layer, it is necessary to analyze the voltage and current waveforms of the fuel cell when the system operates in the EIS detection mode under the second working condition. Figure 17 and Figure 18 As shown, three EIS online tests were performed in the EIS detection mode, and the current of FC#A and FC#B and The AC component amplitudes of the inputs remain at the target values, indicating that the response signal can accurately track the injected AC disturbance signal, ensuring the accuracy of the EIS test results. These HIL simulation results verify that the multi-stack EIS online detection method can be integrated into the EMS at the global level. The system can switch to EIS mode as needed to perform multi-stack EIS testing, providing data support for multi-stack aging consistency assessment. To analyze the accuracy of multi-stack EIS online testing, the theoretical impedance values ​​and test values ​​of FC#A and FC#B are compared, as shown in Table 2.

[0154] Table 2 Theoretical impedance values ​​of FC#A and FC#B and multi-stack EIS online detection HIL simulation measurement values

[0155] ;

[0156] As shown in Table 2, the three test results were averaged and the detection accuracy of FC#A and FC#B in the medium and low frequency bands was calculated to be 99.8% and 99.7%, respectively. This verifies the accuracy of the proposed multi-stack EIS online detection method, which can accurately characterize the electrochemical reaction state and aging degree of multiple stacks online. In order to analyze the feasibility and effectiveness of the power allocation strategy for multiple fuel cell stacks, it is necessary to statistically analyze the output power of the stack in the first and third operating conditions, as shown in Figure 2. Figure 19 and Figure 20 shown.

[0157] like Figure 19 As shown in the figure, compared with the average distribution in the first stage, the output power of the two stacks is evenly distributed in the third stage. Under the premise of ensuring that the stack operates in the linear range, FC#A outputs a larger power and FC#B outputs a smaller power. The statistical results of the output power of each power source are shown in the figure. Figure 20 As shown in the figure, in the third operating condition, 62.7% of the output power of FC#A is concentrated in the range of 800 to 1050 W, while 77.7% of the output power of FC#B is concentrated in the range of 500 to 800 W. This HIL simulation result verifies that the fuel cell multi-stack power allocation strategy can optimize the output power of the stack based on the degree of aging difference of each stack obtained by online detection, which is conducive to improving the aging consistency between stacks and extending the overall life of the power system.

[0158] Therefore, the above HIL simulation results verify the feasibility and effectiveness of the proposed energy hierarchical management strategy considering the aging consistency of multiple stacks. The global layer can switch between various operating modes and complete multi-stack EIS online detection, multi-stack aging consistency assessment and power allocation coefficient λ calculation in the EIS detection mode; the composite energy system layer can manage the lithium battery SOC according to the lithium battery SOC and load power demand, and the lithium battery bears high-frequency power fluctuations, thereby extending the service life of the fuel cell; the fuel cell multi-stack layer optimizes the output power distribution of each sub-stack in the MFCS based on the multi-stack aging consistency assessment results, which is conducive to improving the aging consistency between multiple stacks and extending the overall life of the power system.

Claims

1. A multi-stack fuel cell hybrid system aging consistency assessment method based on EIS, characterized in that: The following steps are involved: S1. Performing electrochemical impedance spectroscopy (EIS) on multiple fuel cell stacks in a multi-stack fuel cell hybrid system to obtain electrochemical impedance spectroscopy data for each fuel cell stack. S2. Based on the acquired electrochemical impedance spectroscopy data, select the impedance values ​​of the frequency bands related to the aging characteristics of the battery stack and calculate the health indicators of each battery stack using a geometric method; In S2, the health index includes HI1. The calculation method of HI1 is: select the impedance points corresponding to 1 Hz and 100 Hz on the electrochemical impedance spectrum, and calculate the abscissa of the intersection of the perpendicular bisector of the line connecting the two points and the real axis, which is HI1. The calculation formula is: ； ； Where, It is the health indicator of the initial battery stack; It is a health indicator of the aging fuel cell stack; is the real part of the impedance of the initial stack at 1 Hz; is the real part of the impedance of the initial stack at 100 Hz; is the imaginary part of the impedance of the initial stack at 1 Hz; is the imaginary part of the impedance of the initial stack at 100 Hz; is the real part of the impedance of the aged stack at 1 Hz; is the real part of the impedance of the aged stack at 100 Hz; is the imaginary part of the impedance of the aged stack at 1 Hz; is the imaginary impedance part of the aged stack at 100 Hz; The health index also includes HI2, which is calculated by selecting impedance points corresponding to 1Hz, 2Hz, 3Hz, 5Hz, 8Hz, 12Hz, 25Hz, 50Hz, and 100Hz on the electrochemical impedance spectrum and calculating the average length of the line connecting each impedance point and the reference point, which is the HI2. The calculation formula is: ； ； Where, The health index of the initial battery stack is calculated based on the geometric characteristics of the electrochemical impedance spectroscopy; is the health indicator of the aged fuel cell stack calculated based on the geometric characteristics of the electrochemical impedance spectroscopy; fre is the detection frequency of the impedance point, i.e., 1Hz, 2Hz, 3Hz, 5Hz, 8Hz, 12Hz, 25Hz, 50Hz, and 100Hz; ZreA-fre is the real part of the impedance of the initial fuel cell stack at the detection frequency of the impedance point, and ZimA-fre is the imaginary part of the impedance of the initial fuel cell stack at the detection frequency of the impedance point; ZreB-fre is the real part of the impedance of the aged fuel cell stack at the detection frequency of the impedance point, and ZimB-fre is the imaginary part of the impedance of the aged fuel cell stack at the detection frequency of the impedance point; S3. Based on the obtained health indicators, evaluate the aging consistency among the multiple fuel cell stacks to obtain an aging consistency evaluation result; In S3, the aging consistency among the plurality of fuel cells is evaluated by comparing the HI1 difference and the HI2 difference of each fuel cell. A larger difference indicates a worse aging consistency among the plurality of fuel cells, and a smaller difference indicates a better aging consistency among the plurality of fuel cells. S4. Based on the obtained aging consistency evaluation results, the output power distribution of each power source is optimized through an energy stratification management strategy, thereby improving the aging consistency of multiple fuel cell stacks.

2. The EIS-based aging consistency assessment method for a multi-stack fuel cell hybrid system according to claim 1, characterized in that: In S1, the electrochemical impedance spectroscopy (EIS) includes two working modes: Power distribution mode: Fuel cells and lithium batteries output power according to energy management strategies to meet load requirements; EIS detection mode: Multiple fuel cells output a fixed power, and the lithium battery supplements the remaining power required by the load or absorbs excess power. In this mode, electrochemical impedance spectroscopy data is collected.

3. The EIS-based aging consistency assessment method for a multi-stack fuel cell hybrid system according to claim 2, characterized in that: In the EIS detection mode, a disturbance signal is applied to the DC / DC converter in the multi-stack fuel cell hybrid system to implement electrochemical impedance spectroscopy (EIS), specifically: For the low frequency band of 1-100Hz, a voltage disturbance signal is superimposed on the set voltage reference of the droop control of the unidirectional DC / DC converter, so that the voltage loop input of the converter has an AC disturbance component; For the high frequency band of 100-2000 Hz, a current disturbance signal is simultaneously superimposed on the input current reference of the unidirectional DC / DC converter, so that the current loop input of the converter has an AC disturbance component.

4. The EIS-based aging consistency assessment method for a multi-stack fuel cell hybrid system according to claim 3, characterized in that: The amplitude of the disturbance signal is controlled, specifically by selecting a signal with a relatively large amplitude in the AC components of the output current of the two fuel cells as a reference signal, and using the amplitude calculated in real time as a feedback value for closed-loop control of the disturbance signal amplitude, and the amplitudes of the AC components of the output current of the two fuel cells do not exceed 10% of the DC output current of each.

5. An energy management method for aging consistency of a multi-stack fuel cell hybrid system based on EIS, characterized in that: The following steps are involved: Formulate an energy management strategy through a hierarchical finite state machine based on the evaluation results obtained by the EIS-based aging consistency evaluation method for a multi-stack fuel cell hybrid system according to any one of claims 1 to 4; The energy management strategy formulated by the hierarchical finite state machine includes a global layer, a composite energy system layer and a fuel cell multi-stack layer; The power distribution mode and the EIS detection mode are switched according to the global layer. The power distribution mode is operated normally, and the EIS detection mode is switched to when the EIS detection is required. The composite energy system layer optimizes the power distribution between the multi-stack power system and the auxiliary power source according to the charge state of the auxiliary power source and the load power demand; The fuel cell multi-stack layer optimizes the output power distribution between each stack based on the aging consistency evaluation results and the output power required by the multi-stack power system, thereby improving the aging consistency of the multi-stack.

6. The energy management method for aging consistency of a multi-stack fuel cell hybrid system based on EIS according to claim 5, characterized in that: In the EIS detection mode of the global layer, multiple fuel cells output fixed power, and the auxiliary power source supplements the remaining power required by the load or absorbs excess power; in the power distribution mode, power distribution is completed collaboratively by the composite energy system layer and the fuel cell multi-stack layer.

7. The energy management method for aging consistency of a multi-stack fuel cell hybrid system based on EIS according to claim 5, characterized in that: The power distribution principle of the fuel cell multi-stack layer is: the power distribution coefficient is calculated based on the aging consistency evaluation results. λ ,based on λ The power distribution is divided into light-load range, medium-load range and heavy-load range according to the output power required by the multi-stack fuel cell hybrid system; in the light-load range, the fuel cell stack with a more serious aging degree outputs the minimum power in the linear working area, and the healthy fuel cell stack outputs a relatively large power; in the medium-load range, the healthy fuel cell stack outputs a large power, and the fuel cell stack with a more serious aging degree outputs a small power; in the heavy-load range, the healthy fuel cell stack outputs the maximum power in the linear working area, and the fuel cell stack with a more serious aging degree outputs a relatively small power; and each fuel cell stack works in the linear working area to improve the aging consistency between multiple fuel cell stacks.

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