A method for quantifying the health status of proton exchange membrane fuel cells

Abstract

The present invention discloses a method for quantifying the health status of a proton exchange membrane fuel cell, and belongs to the field of hybrid power energy distribution. The method for quantifying the health status of a proton exchange membrane fuel cell of the present invention simultaneously considers the water content and the voltage actually output by the stack and its rate of change. The two factors are cross-coupled and work together to more accurately and real-time reflect the health status of the PEMFC, and has broad engineering practice significance. The method of the present invention only requires measuring the relative humidity of the gas and the actual output voltage of the stack to obtain the health status of the PEMFC, and the operation is simple. Based on the method for quantifying the health status of a proton exchange membrane fuel cell, the present invention can simultaneously control the health status of the PEMFC during the power distribution process of the hybrid power system, thereby delaying its performance degradation and increasing the operating life of the PEMFC.

Description

Technical Field

[0001] The present invention belongs to the field of hybrid power energy distribution, and more specifically, relates to a method for quantifying the health status of a proton exchange membrane fuel cell. Background Art

[0002] Proton Exchange Membrane Fuel Cell (PEMFC) is clean, efficient and pollution-free, and has been widely used in the field of new energy vehicles. PEMFC works at room temperature and is easier to use than other fuel cells. PEMFC is small in size, light in weight and has low noise, making it very suitable for new energy vehicles.

[0003] At present, many scholars have studied Figure 1 The research direction of the hybrid power system of PEMFC and lithium battery shown in the figure mainly focuses on the energy distribution method, but the traditional energy distribution strategy often only focuses on how to coordinate the power distribution between PEMFC and lithium battery, ignoring the attenuation of the performance of PEMFC and lithium battery, and does not quantify the service life of PEMFC and lithium battery. In order to maintain the normal operation of the hybrid system for a long time, it is necessary to control the health of PEMFC and lithium battery during the power distribution process. In addition, these previous powertrain configurations are designed to allow the fuel cell group to provide most of the power demand and use the battery only as a buffer for high transient power demand. Different power management strategies usually lead to different load cycles, which may significantly affect the life of the fuel cell group. Therefore, it is very important to understand the degradation mechanism of the fuel cell group and improve its durability by incorporating these mechanisms into the power management system. Proton exchange membrane fuel cell (PEMFC) performance may decline due to various reasons. The attenuation of PEMFC refers to the result of this situation. In the attenuation of PEMFC, some performance can be restored by some remedial measures, but some performance is permanently lost. In order to reduce the attenuation of PEMFC as much as possible and ensure that PEMFC has a long service life, the research on PEMFC health is becoming more and more popular. At the same time, in order to ensure that PEMFC works efficiently for a long time, higher requirements are also put forward for PEMFC hybrid power system. If the long life of PEMFC cannot be guaranteed, then PEMFC will need to replace the stack frequently in actual use, which is not only inconvenient for the use of PEMFC, but also greatly increases the cost of using PEMFC. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a method for quantifying the health status of a proton exchange membrane fuel cell, the purpose of which is to quantify the service life of PEMFC and then to manage its health during the power distribution process of the hybrid system.

[0005] To achieve the above object, according to one aspect of the present invention, a method for quantifying the health status of a proton exchange membrane fuel cell is provided, comprising:

[0006] S1. Constructing an ECSA attenuation model; the ECSA attenuation model includes a water content influence sub-model, a voltage influence sub-model and a catalyst layer gas flow channel sub-model; wherein the water content influence sub-model is used to calculate the influence factor of water content on ECSA under the current operating state of the proton exchange membrane fuel cell;

[0007] The voltage influence sub-model is used to calculate the influence factors of the actual output voltage of the proton exchange membrane fuel cell stack and its change rate on ECSA;

[0008] The catalyst layer gas flow channel sub-model is used to calculate the current ECSA of the catalyst layer of the proton exchange membrane fuel cell according to the product of the influence factor of the water content affecting the sub-model output and the influence factor of the voltage affecting the sub-model output;

[0009] S2. Under normal working conditions of the proton exchange membrane, calculate the water content value inside the fuel cell stack and obtain the current fuel cell stack output voltage and its rate of change, input them into the ECSA attenuation model, and obtain the current health status of the proton exchange membrane fuel cell.

[0010] Furthermore, the water content affects the modeling process of the sub-model.

[0011] Calculate the water content corresponding to different relative humidity of gases in the proton exchange membrane;

[0012] Measure the change of ECSA over time at different water contents;

[0013] The influence factor of water content on ECSA change was obtained according to the slope of the logarithm of ECSA change over time.

[0014] Furthermore, the water content is calculated as follows:

[0015]

[0016]

[0017] λ m represents the water content at the proton exchange membrane, a m represents the relative humidity at the proton exchange membrane, P v,m represents the water vapor partial pressure at the proton exchange membrane, P sat,mrepresents the atmospheric pressure at the proton exchange membrane.

[0018] Furthermore, the catalyst layer gas flow sub-model uses Calculate ECSA;

[0019] s min is the minimum platinum surface area, N is the operating time of the proton exchange membrane fuel cell, k total The product of the influence factor of the water content affecting the sub-model output and the influence factor of the voltage affecting the sub-model output, s(N) represents ECSA.

[0020] The present invention also provides a method for quantifying the health status of a power system of a hybrid PEMFC and a lithium battery, comprising:

[0021] The health status of a proton exchange membrane fuel cell is evaluated by using the above-mentioned proton exchange membrane fuel cell health status quantification method;

[0022] Use SOC to assess lithium-ion health status.

[0023] The present invention also provides a method for distributing energy in a power system of a hybrid of PEMFC and lithium battery, comprising:

[0024] The stack output voltage and its change rate are regulated so that the proton exchange membrane fuel cell follows the load demand while minimizing the ECSA attenuation rate; wherein the ECSA attenuation rate is calculated using the above-mentioned proton exchange membrane fuel cell health status quantification method.

[0025] Furthermore, a neural network predictive controller is used to regulate the stack output voltage and its rate of change.

[0026] Preferably, the following optimization objectives are used to regulate the stack output voltage and its rate of change:

[0027] J=q(P ref -P FC ) 2 +r△P FC 2

[0028] Among them, P ref Indicates the required power, P FC Indicates the actual output power of PEMFC, △P FC It represents the change of the actual output power of PEMFC between two sampling moments, q and r represent the weights of the two items respectively.

[0029] In general, compared with the prior art, the above technical solution conceived by the present invention can achieve the following beneficial effects.

[0030] (1) The method for quantifying the health status of a proton exchange membrane fuel cell of the present invention takes into account the water content and the voltage and its rate of change of the actual output of the stack. The two factors are cross-coupled and work together to more accurately and real-time reflect the health status of the PEMFC, which has broad engineering practice significance. The method of the present invention only requires measuring the relative humidity of the gas and the actual output voltage of the stack to obtain the health status of the PEMFC, and the operation is simple.

[0031] (2) During the power distribution process of the hybrid power system, the present invention simultaneously controls the health status of the PEMFC, thereby delaying its performance degradation and increasing the operating life of the PEMFC. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is the topological structure diagram of the hybrid power system;

[0033] Figure 2 is the ECSA decay model;

[0034] Figure 3 It is a system model based on MPC controller;

[0035] Figure 4 It is a system model based on NNPC controller;

[0036] Figure 5 The ECSA decay of the proton exchange membrane is affected by the low current ratio when RH = 0.5;

[0037] Figure 6 When the low current accounts for 50%, the ECSA decay is affected by the RH of the proton exchange membrane;

[0038] Figure 7 When the low current accounts for 50%, the ECSA decay rate is affected by the RH of the proton exchange membrane;

[0039] Figure 8 How the power demand of the hybrid system changes over time under WLTC conditions;

[0040] Fig. 9 The demand power of the hybrid system changes with time under CLTC-P conditions;

[0041] Fig.10 The demand power of the hybrid system changes over time under EPA conditions;

[0042] Fig.11 The demand power of the hybrid system changes with time under NYCC conditions;

[0043] Fig.12Under the WLTC condition, the RH values ​​of the proton exchange membrane were set to 0.5, 0.6, 0.7, 0.8 and 0.9, and the MPC control algorithm was used to regulate the hybrid system to obtain the change of the ECSA of the PEMFC over time;

[0044] Fig.13 Under the CLTC-P condition, the RH values ​​of the proton exchange membrane were set to 0.5, 0.6, 0.7, 0.8 and 0.9, and the hybrid system was regulated by the MPC control algorithm to obtain the change of the ECSA of the PEMFC over time;

[0045] Fig.14 Under EPA conditions, the RH values ​​of the proton exchange membrane were set to 0.5, 0.6, 0.7, 0.8 and 0.9, and the hybrid system was regulated by the MPC control algorithm to obtain the changes of the ECSA of the PEMFC over time;

[0046] Fig.15 Under NYCC conditions, the RH values ​​of the proton exchange membrane were set to 0.5, 0.6, 0.7, 0.8 and 0.9, and the hybrid system was regulated by the MPC control algorithm to obtain the changes of the ECSA of the PEMFC over time;

[0047] Fig.16 Under the WLTC condition, the RH values ​​of the proton exchange membrane were set to 0.5, 0.6, 0.7, 0.8 and 0.9, and the NNPC control algorithm was used to regulate the hybrid system to obtain the change of the ECSA of the PEMFC over time;

[0048] Fig.17 Under the CLTC-P condition, the RH values ​​of the proton exchange membrane were set to 0.5, 0.6, 0.7, 0.8 and 0.9, and the hybrid system was regulated by the NNPC control algorithm to obtain the change of the ECSA of the PEMFC over time;

[0049] Fig.18 Under EPA conditions, the RH values ​​of the proton exchange membrane were set to 0.5, 0.6, 0.7, 0.8 and 0.9, and the NNPC control algorithm was used to regulate the hybrid system to obtain the changes in the ECSA of the PEMFC over time;

[0050] Fig.19 Under NYCC conditions, the RH values ​​of the proton exchange membrane were set to 0.5, 0.6, 0.7, 0.8 and 0.9, and the NNPC control algorithm was used to regulate the hybrid system to obtain the change of the ECSA of the PEMFC over time;

[0051] Fig. 20Under WLTC conditions, the hybrid system is regulated by MPC and NNPC control algorithms to obtain the change of PEMFC ECSA over time;

[0052] Fig.21 Under WLTC conditions, the hybrid system is regulated by MPC and NNPC control algorithms to obtain the change of SOC of the lithium battery over time;

[0053] Fig. 22 Under CLTC-P conditions, the hybrid system is regulated by MPC and NNPC control algorithms to obtain the change of PEMFC ECSA over time;

[0054] Fig.23 Under the CLTC-P condition, the MPC and NNPC control algorithms are used to regulate the hybrid system to obtain the change of SOC of the lithium battery over time;

[0055] Fig.24 Under EPA conditions, the hybrid system is regulated by MPC and NNPC control algorithms to obtain the change of PEMFC ECSA over time;

[0056] Fig.25 Under EPA conditions, the hybrid system is regulated by MPC and NNPC control algorithms to obtain the change of SOC of the lithium battery over time;

[0057] Fig.26 Under NYCC conditions, the hybrid system is regulated by MPC and NNPC control algorithms to obtain the change of PEMFC ECSA over time;

[0058] Fig. 27 Under NYCC conditions, the hybrid system is regulated by MPC and NNPC control algorithms to obtain the change of SOC of the lithium battery over time;

[0059] Fig.28 Under WLTC conditions, the hybrid power system is regulated by using MPC and NNPC control algorithms to obtain the output power variation of PEMFC over time;

[0060] Fig.29 Under CLTC-P conditions, the hybrid power system is regulated by using MPC and NNPC control algorithms to obtain the output power variation of PEMFC over time;

[0061] Fig.30 Under EPA conditions, the hybrid power system is regulated by using MPC and NNPC control algorithms to obtain the output power variation of PEMFC over time;

[0062] Fig.31 Under NYCC conditions, the MPC and NNPC control algorithms are used to regulate the hybrid power system and obtain the output power variation of PEMFC over time. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0064] When the water content in the PEM fuel cell changes, it will directly affect the performance of the PEM, thus causing the output power of the PEM fuel cell to change. At the same time, the change in water content causes the ECSA attenuation rate to change. When the ECSA changes, the output power of the PEM fuel cell will also change. When the output power of the PEM fuel cell changes, the total amount of water obtained by the chemical reaction of hydrogen and oxygen inside the PEM fuel cell will change, causing the water content of the PEM fuel cell to change.

[0065] The present invention focuses on the water content of the proton exchange membrane and the external load current demand. Figure 2 The ECSA decay model of PEMFC is shown in Figure 1. The model process and ideas of ECSA decay are as follows: The ECSA decay model includes a sub-model describing the effect of water content on ECSA decay, a sub-model describing the effect of voltage on ECSA decay, and a sub-model describing the gas flow channel of the catalyst layer.

[0066] The sub-model describing the ECSA decay affected by water content is obtained by obtaining different membrane water contents λ m An empirical model for the effects of ECSA attenuation. Membrane water content λ m It is also related to the relative humidity of the gas at the proton exchange membrane, so the specific sub-model construction method is to obtain their influence on ECSA attenuation when the relative humidity of the gas at the proton exchange membrane is 50% and 100%, respectively, and then use linear interpolation to obtain the empirical formula of water content on ECSA attenuation. The inputs of the sub-model describing the ECSA attenuation affected by water content include: intake humidity, gas flow rate, current, etc. The output of the sub-model describing the ECSA attenuation affected by water content is the water content influencing factor. Through this sub-model, it is possible to ensure the health of PEMFC by controlling the water content of PEMFC within a certain range, so as to achieve the purpose of its health control.

[0067] The membrane water content λ m Generally, it cannot be obtained by direct measurement, and the present invention uses indirect calculation. Indirect calculation requires the relative humidity of the incoming gas. Assuming that the relative humidity RH of the gas at the outlet of the anode and cathode of the PEMFC is 0.5 in the initial state, and the humidity RH of the inlet gas is set as an adjustable value, the inlet water vapor partial pressure of the anode and cathode is shown in formula (1):

[0068] P v,i =RH×P sat,i , (1)

[0069] P sat,i represents the ambient atmospheric pressure at location i, for example, P sat,m Represents the atmospheric pressure at the proton exchange membrane; P v,i represents the partial pressure of water vapor at point i, for example: P v,in represents the water vapor partial pressure at the inlet, P v,out represents the partial pressure of water vapor at the outlet, P v,m represents the partial pressure of water vapor at the proton exchange membrane. sat,i It can be calculated according to the empirical formula (2) recommended by Emanuel, and its unit is Pa:

[0070]

[0071] Where T represents the temperature at i. Assuming that the water vapor partial pressure from the anode inlet to the cathode outlet presents a linear increasing relationship, the water vapor partial pressure in the anode and cathode gases is as shown in formula (3):

[0072]

[0073] P v,in Indicates the water vapor pressure at the inlet, P v,out Indicates the water vapor pressure at the outlet. Since the distance from the inlet to the outlet of the anode and cathode is relatively short, the present invention adopts P v Instead of P v,m . After getting P v,m After that, the water content of the proton exchange membrane m Using empirical formulas (4) and (5), we can obtain:

[0074]

[0075]

[0076] λ m represents the water content at the proton exchange membrane, a m represents the relative humidity at the proton exchange membrane, P sat,m represents the atmospheric pressure at the proton exchange membrane.

[0077] The membrane water content λ is obtained from m Then calculate the membrane water content λ m Impact on ECSA attenuation.

[0078] The sub-model describing the voltage effect on ECSA attenuation is an empirical model summarized by obtaining the effects of different voltages and voltage change rates on ECSA attenuation.

[0079] The sub-model describing the gas flow channel of the catalyst layer is an empirical model that quantifies the physical description of the catalyst layer, specifically including the set total surface area of ​​the Pt particles, the oxygen flow rate input to the flow channel, the hydrogen flow rate input to the flow channel, and the maximum current density that can pass through the catalyst layer.

[0080] According to the empirical formula, when the water content is smaller, the time consumed by ECSA to decay to the minimum value is longer; when the voltage is smaller and the voltage change rate is smaller, the time consumed by ECSA to decay to the minimum value is longer. Therefore, in order to maintain the healthy state of PEMFC in the hybrid system, it is necessary to maintain a lower water content, a lower voltage and a lower voltage change rate.

[0081] The relationship between the remaining active surface area S(N) and the initial total active surface area S0 is constructed as shown in formula (6):

[0082]

[0083] k represents the impact factor, N represents the number of cycles, which can also be replaced by time / s. When the humidity is 50%, by fitting the actual data, k = 2.05 × 10 -4 min -1 When the humidity is 100%, by fitting the actual data, we get k = 3.72 × 10 -4 min -1 .set up When ECSA continues to degrade, there is a minimum s(N), denoted as s min . min It is defined as the minimum platinum surface area and is taken as 0.2. The relationship between the two is shown in formula (7):

[0084]

[0085] k total =k SC ×k T ×k RH ×k UPL ×k dwell , (8)

[0086] In formula (7), ktotal Represents the total degradation rate of all influencing factors, k SC is the degradation rate of the standard voltage cycle, k T is the ECSA decay rate factor caused by temperature, k RH is the ECSA decay rate factor caused by relative humidity, k UPL is the ECSA attenuation rate factor caused by the upper voltage limit, k dwell k is the ECSA decay rate factor caused by voltage duration. The effects of temperature, humidity, voltage, etc. on ECSA degradation in PEMFC can be studied independently. UPL , k dwell The calculation formulas are shown in equations (9) and (10):

[0087] k UPL =e C×(UPL-0.95) , (9)

[0088] k dwell =0.38+0.29×t dwell , (10)

[0089] In the formula, C is a constant value, which is 0.00152mV -1 , UPL represents the local maximum value of voltage during the sampling period, t dwell Indicates the time duration that the voltage is maintained at the local minimum value during the adoption cycle. In this invention, only the relationship between relative humidity, voltage and the degradation rate of ECSA in PEMFC is studied, that is, assuming that k SC =k T =1.

[0090] MPC refers to model predictive control, which is a feedback control algorithm that uses a model to predict the future output of the process. MPC is a multivariable controller that controls the output while considering the interaction between all system variables. MPC mainly consists of four parts: object module, prediction module, rolling optimization module, and feedback correction module. What is different from traditional PID feedback control is that at each moment, MPC will learn about possible future situations through the prediction module, and generally use the prediction step length to show the length of MPC's prediction of the future. At the same time, in these situations, the optimal control solution is found. The control step length is generally used to show the range of MPC's search for the optimal solution, and the rolling module is used to achieve the function of making the best decision at each sampling point. The mechanism of MPC is: at each sampling moment, according to the current system state and measurement information, based on the prediction results of the future state of the prediction model, a finite-time optimal control problem is solved online, and the control behavior at the current moment and in the future period is obtained, and only the control behavior at the current moment is executed. At the next sampling moment, the above process is repeated according to the system state after control at the previous moment and the new measurement information.

[0091] The design of MPC is divided into four parts: the construction of model module, the construction of prediction module, the construction of rolling optimization module and the construction of feedback correction module. In the construction of model module, this paper adopts step response model, that is, inputting step signal into the built object module and recording the output signal of the object module, thus forming a one-to-one correspondence between input and output.

[0092] Neural network predictive control predicts the process output of the model by using a neural network. Neural network predictive control uses neural network modeling to replace the object module part of MPC. The present invention inputs a series of random rectangular wave signals into the PEMFC system and collects the output signal of the system. The input signal and the output signal are matched one by one as the data set of the neural network predictive model. After multiple rounds of training, the neural network predictive model that shows the smallest mean square error on the neural network training set is selected as the object module of the controller.

[0093] Assume that the increment of the input signal at time k is △u(k), the increment of the output signal is △y(k), the prediction step length of MPC and NNPC is P steps, and the control step length is M steps. Then each △y(k) can be represented by the △u of the previous n moments, and the relationship is shown in formula (11):

[0094]

[0095] In the construction of the prediction module, since the prediction step length is P steps, for P moments, the system output increment at each moment is shown in formula (12):

[0096]

[0097] Among them, a i is the coefficient, i=1,2,3,...,M. In the rolling optimization module, this paper adopts the quadratic programming method to obtain the optimal solution. Suppose the expected output at time k+i obtained by MPC control is w(k+i), and the current output and target output of the system are y(k) and y r . Then the expected output can be expressed by formula (13):

[0098] w(k+i)=α i y(k)+(1-α i )y r , (13)

[0099] Among them, α is the coefficient, i=1,2,3,...,M.

[0100] The present invention ensures the power following of PEMFC, reduces the fluctuation of PEMFC as much as possible, maintains the SOC of lithium battery, and optimizes the objective function as J=q(P ref -P FC ) 2 +r△P FC 2 Among them, P ref Indicates the required power, P FC Indicates the actual output power of PEMFC, △P FC represents the change in the actual output power of the PEMFC between two sampling moments, q and r represent the weights respectively. The optimization scheme of the present invention seeks the minimum value of the optimization objective function.

[0101] The function solved by quadratic programming in the present invention can be expressed as formula (14):

[0102]

[0103] In order to ensure the power output of PEMFC, reduce the fluctuation of PEMFC and maintain the SOC of lithium battery, the optimization target of the present invention is as shown in formula (15):

[0104] J=q(P ref -P FC ) 2 +r△P FC 2 , (15)

[0105] Among them, P ref Indicates the required power, P FC Indicates the actual output power of PEMFC, △P FCrepresents the change in the actual output power of PEMFC between two sampling moments, q and r represent the weights of the two items respectively. The constraints that need to be considered are shown in formula (16):

[0106]

[0107] In the construction of the feedback correction module, the error e(k+1) at time k+1 can be expressed as formula (17):

[0108]

[0109] y(k+1) represents the true value at time k+1, represents the predicted value at time k+1. Then the compensation value y at time k+1, k+2, ..., k+P is cor It can be shown by formula (18):

[0110]

[0111] h i is the correction coefficient, i = 1, 2, 3, ..., P. Then, at each scroll, the value at the next moment only needs to be multiplied by the displacement matrix based on the current value.

[0112] The present invention designs a controller based on MPC and NNPC. Then, the two control schemes are used to compare the simulation results under different working conditions of WLTP, CLTC-P, EPA and NYCC, and the two control schemes are compared vertically under a single working condition.

[0113] according to Figure 3 , 4 The hybrid power system model structure diagram shown in the figure applies the proposed hybrid power energy distribution strategy considering health control to the Simulink simulation platform of Matlab. This section conducts experimental verification based on the built hybrid power system. While keeping the proton exchange membrane RH = 0.5, change the proportion of low current in the input current. The low current proportion starts from 10% of the rectangular wave current cycle, and each experiment increases by 10% of the full cycle, gradually increasing to 90% of the low current proportion of the full cycle. Record the experimental simulation data and draw a graph as shown in Figure 5 As shown. While keeping the low current duty cycle at 50%, change the RH of the proton exchange membrane. Starting from RH = 0.5, increase by 0.1 in each experiment, and gradually increase to RH = 0.9. Record the experimental simulation data and draw the graph and decay rate graph as shown Figure 6 , 7 shown.

[0114] from Figure 5It can be found that under the condition of proton exchange membrane RH = 0.5, when the low current accounts for 10%, it takes about 1.8×10 7 seconds, that is, 5,000 hours. When the low current accounts for 30%, it takes about 0.9×10 7 seconds, that is, 2.5 thousand hours. When the low current accounts for 90%, it takes about 0.4×10 7 Seconds is 1.1 thousand hours. As the proportion of low current increases, the time it takes for ECSA to decay to the minimum value is significantly reduced. This shows that when the RH of the proton exchange membrane remains unchanged, the longer the low current output by PEMFC is, the more unhealthy it is for PEMFC, and long-term low current will significantly reduce the ECSA of PEMFC. So in order to keep the ECSA of PEMFC at a healthy level for a long time, when the RH of the proton exchange membrane remains unchanged, PEMFC needs to output less low current. It should be noted here that when PEMFC outputs low current, it means that PEMFC will also output high voltage, and when PEMFC outputs high current, it means that PEMFC will also output low voltage.

[0115] from Figure 6 It can be found that when the low current accounts for 50%, when the proton exchange membrane RH = 0.5, it takes about 1.3×10 7 seconds, that is, 3.6 thousand hours. When the proton exchange membrane RH = 0.7, it takes about 1.0×10 7 seconds, that is, 2.8 thousand hours. When the proton exchange membrane RH = 0.9, it takes about 0.7×10 7 seconds, or 1.9 thousand hours. As the RH of the proton exchange membrane increases, the ECSA value decreases at the same time. This shows that when the output current is the same, the greater the RH of the proton exchange membrane in the PEMFC, the less healthy it is for the PEMFC. Long-term high RH will significantly reduce the ECSA of the PEMFC. At the same time, Figure 7 It can be found that the decay rate of ECSA has a turning point. Before this turning point, the greater the humidity, the greater the decay rate of ECSA. After this turning point, the greater the humidity, the smaller the decay rate of ECSA. In order to keep the ECSA of PEMFC at a healthy level for a long time and ensure the normal operation of PEMFC, the proton exchange membrane in PEMFC needs to maintain low RH when it is not in a dry membrane state while keeping the output current unchanged. It should be noted here that if the proton exchange membrane is in a dry membrane state, it means that the proton exchange membrane can no longer work normally.

[0116] Currently, the commonly used urban operating conditions are WLTP, CLTC-P, EPA and NYCC. By plotting the equivalent power of the vehicle speed under these conditions, we can get the change of the required power of the hybrid system over time. The results are as follows: Figure 8 , 9 , 10 and 11. After converting the instantaneous speed of the vehicle into the instantaneous power of the generator, the system is controlled by MPC and NNPC. Assume that the maximum discharge power of the lithium battery is 3C, the maximum charging power is 0.5C, and in addition to the SOC needing to be maintained at SOC min To SOC max There are no other restrictions. When the output power of PEMFC is greater than the required power, the excess power is used to charge the lithium battery. When the output power of PEMFC is less than the required power, the missing power is supplemented by the discharge of the lithium battery.

[0117] Under WLTC, CLTC-P, EPA and NYCC conditions, the RH values ​​of the proton exchange membrane are set to 0.5, 0.6, 0.7, 0.8 and 0.9 respectively. The MPC control algorithm is used to regulate the hybrid system, and the ECSA of the PEMFC changes with time as shown in the figure. Fig.12 , 13 , 14 and 15. Under the WLTC condition, ECSA finally decays to 0.998478 when RH = 0.5, 0.998231 when RH = 0.6, 0.997982 when RH = 0.7, 0.997734 when RH = 0.8, and 0.997485 when RH = 0.9. It can be found that under the WLTC condition, when the MPC control algorithm is used, when the RH of the proton exchange membrane increases, the value of ECSA after the same time decreases. Under the CLTC-P, EPA, and NYCC conditions, it can also be found from the figure that there is the same change trend as under the WLTC condition. Therefore, when the MPC control method is used under these four conditions, the value of ECSA will gradually decrease as the RH slowly increases.

[0118] Under WLTC, CLTC-P, EPA and NYCC conditions, the RH values ​​of the proton exchange membrane are set to 0.5, 0.6, 0.7, 0.8 and 0.9 respectively. The NNPC control algorithm is used to regulate the hybrid system, and the ECSA of the PEMFC changes with time as shown in the figure. Fig.16 , 17, 18 and 19. Under the WLTC condition, ECSA finally decays to 0.998559 when RH=0.5, 0.998324 when RH=0.6, 0.998089 when RH=0.7, 0.997853 when RH=0.8, and 0.997618 when RH=0.9. It can be found that under the WLTC condition, when the MPC control algorithm is used, when the RH of the proton exchange membrane increases, the value of ECSA after the same time decreases. Under the CLTC-P, EPA, and NYCC conditions, it can also be found from the figure that there is the same change trend as under the WLTC condition. Therefore, under these four conditions, the NNPC control method is used, and the value of ECSA will gradually decrease as the RH slowly increases.

[0119] Under the condition of proton exchange membrane RH = 0.5, the operating conditions are set as WLTC, CLTC-P, EPA and NYCC. The MPC and NNPC control algorithms are used to regulate the hybrid system, and the changes of PEMFC ECSA over time and the changes of lithium battery SOC over time are obtained as shown in Fig. 20 , 21 , 22, 23, 24, 25, 26 and 27. Under the WLTC condition, using NNPC regulation, ECSA finally decays to 0.998559, and the mean square error of SOC fluctuation is 7.84×10 -6 , using MPC control, ECSA finally decays to 0.998478, and the mean square error of SOC fluctuation is 4.26×10 -6 Under the CLTC-P condition, using NNPC regulation, ECSA finally decays to 0.998271, and the mean square error of SOC fluctuation is 0.71×10 -6 , using MPC control, ECSA finally decays to 0.998171, and the mean square error of SOC fluctuation is 1.82×10 -6 Under EPA conditions, using NNPC regulation, ECSA eventually decayed to 0.998821, and the mean square error of SOC fluctuation was 3.20×10 -7 , using MPC control, ECSA finally decays to 0.998759, and the mean square error of SOC fluctuation is 6.18×10 -7 Under NYCC conditions, using NNPC regulation, ECSA finally decays to 0.999325, and the mean square error of SOC fluctuation is 6.50×10 -8 , using MPC control, ECSA finally decays to 0.999285, and the mean square error of SOC fluctuation is 8.17×10 -8It can be found that compared with MPC, NNPC can not only better alleviate the attenuation of ECSA, but also better alleviate the SOC fluctuation of lithium batteries in most cases.

[0120] Under the condition of proton exchange membrane RH = 0.5, the operating conditions are set as WLTC, CLTC-P, EPA and NYCC. The MPC and NNPC control algorithms are used to regulate the hybrid system, and the output power of PEMFC changes with time as shown in the figure. Fig.28 , 29 , 30 and 31. It can be seen from the figure that both MPC and NNPC can adjust the system in time, and have good adjustment ability and strong robustness.

[0121] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quantifying the health status of a proton exchange membrane fuel cell, characterized in that: include: S1. Construct ECSA attenuation model; The ECSA attenuation model includes a water content influence sub-model, a voltage influence sub-model and a catalyst layer gas flow channel sub-model; wherein, The water content influence sub-model is used to calculate the influence factor of water content on ECSA under the current operating state of the proton exchange membrane fuel cell; the modeling process of the water content influence sub-model includes: calculating the water content corresponding to the relative humidity of different gases in the proton exchange membrane, and the calculation formula of the water content is: λ m represents the water content at the proton exchange membrane, a m represents the relative humidity at the proton exchange membrane, P v,m represents the water vapor partial pressure at the proton exchange membrane, P sat,m Indicates the atmospheric pressure at the proton exchange membrane; measures the relationship between the change of ECSA with time under different water contents; obtains the influence factor of water content on the change of ECSA based on the slope of the logarithm of the change of ECSA with time; The voltage influence sub-model is used to calculate the influence factors of the actual output voltage of the proton exchange membrane fuel cell stack and its change rate on ECSA; The catalyst layer gas flow channel sub-model is used to multiply the influence factor of the water content on the sub-model output and the influence factor of the voltage on the sub-model output. Calculate the current ECSA of the catalyst layer of the proton exchange membrane fuel cell, s min is the minimum platinum surface area, N is the operating time of the proton exchange membrane fuel cell, k total The product of the influence factor of water content on the sub-model output and the influence factor of voltage on the sub-model output, s(N) represents ECSA; S2. Under normal working conditions of the proton exchange membrane, calculate the water content value inside the fuel cell stack and obtain the current fuel cell stack output voltage and its rate of change, input them into the ECSA attenuation model, and obtain the current health status of the proton exchange membrane fuel cell.

2. A method for quantifying the health status of a power system hybrid of PEMFC and lithium battery, characterized in that: include: Quantifying the health status of a proton exchange membrane fuel cell using the method of claim 1; Use SOC to assess lithium-ion health status.

3. A method for distributing energy in a power system of a hybrid of PEMFC and lithium battery, characterized in that: include: The stack output voltage and its change rate are regulated so that the proton exchange membrane fuel cell follows the load demand while minimizing the ECSA attenuation rate; wherein the ECSA attenuation rate is calculated using the method described in claim 1.

4. The energy distribution method of a PEMFC and lithium battery hybrid power system according to claim 3, characterized in that: A neural network predictive controller is used to regulate the stack output voltage and its rate of change.

5. The energy distribution method of a power system of a hybrid of PEMFC and lithium battery according to claim 3 or 4, characterized in that: The following optimization objectives are used to control the stack output voltage and its rate of change; J=q(P ref -P FC ) 2 +r△P FC 2 Among them, P ref Indicates the required power, P FC Indicates the actual output power of PEMFC, △P FC It represents the change of the actual output power of PEMFC between two sampling moments, q and r represent the weights of the two items respectively.

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