Thermal balance control method based on fuel cell multi-mode node thermal model

Abstract

The present invention relates to a thermal balance control method based on a multi-mode node thermal model of a fuel cell, comprising the following steps: Step S1: constructing a multi-mode node physical thermal model based on the spatial distribution of fuel cell temperature, including models of n nodes to form a model library; Step S2: designing a corresponding model predictive controller based on the multi-mode node physical thermal model using a linear method to adjust the temperature of a specific area; Step S3: constructing an adaptive switching controller to select in real time the area in the model library with the largest temperature difference from other nodes and perform corrective control. When the temperature of the area reaches a preset value, the controller switches to the next model node for temperature correction, thereby reducing the temperature difference in the temperature field and increasing the fuel cell voltage under the condition of the same output current. The present invention reduces the temperature difference inside the fuel cell and makes the temperature field more uniform, thereby improving the output performance of the fuel cell.

Description

Technical Field

[0001] The present invention relates to the field of fuel cell temperature control, and in particular to a thermal balance control method based on a fuel cell multi-mode node thermal model. Background Art

[0002] Hydrogen fuel cells are highly efficient and clean, capable of stable, uninterrupted continuous output. Compared to other renewable energy sources such as solar, wind, and hydropower, they can overcome the inherent limitations of intermittent, volatile, and random systems. In recent years, hydrogen fuel cell micro-combined heat and power (Micro-CHP) systems have been considered as potential replacements for traditional household gas boilers and have significantly reduced long-distance power transmission losses (approximately 8%-10%), district heating transmission losses (approximately 10%-15%), energy losses, and exhaust emissions. Consequently, they have attracted attention from both industry and academia. However, due to the lack of breakthroughs in key technologies such as hydrogen fuel cells and their management systems, the energy efficiency, durability, stability, and service life of hydrogen fuel cell-related systems are limited. Furthermore, factors such as the high cost of hydrogen production have prevented the widespread promotion of hydrogen fuel cells. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a thermal balance control method based on a fuel cell multi-mode node thermal model to reduce the temperature difference inside the fuel cell and make the temperature field distribution more uniform, thereby improving the output performance of the fuel cell.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A thermal balance control method based on a fuel cell multi-mode node thermal model comprises the following steps:

[0006] Step S1: Based on the spatial distribution of fuel cell temperature, a multi-mode node physical thermal model is constructed, including models of n nodes, forming a model library;

[0007] Step S2: Based on the multi-mode node physical thermal model, a linear method is used to design a corresponding model predictive controller to adjust the temperature of a specific area;

[0008] Step S3: Construct an adaptive switching controller to select the area in the model library with the largest temperature difference from other nodes in real time for correction control. When the temperature of the area reaches the preset value, switch to the next model node for temperature correction, thereby reducing the temperature difference in the temperature field and increasing the fuel cell voltage under the same output current.

[0009] Furthermore, the step S1 is specifically as follows:

[0010] Step S11: Analyze the fuel cell characteristics, derive its working model, and determine the operating relationship between the fuel cell heat generation power and heat dissipation power as follows:

[0011]

[0012] Where m fc is the total mass of the fuel cell, C fc is the specific heat capacity, T fc is the temperature of the fuel cell, is the total heat change rate generated by the fuel cell chemical reaction, P elec is the fuel cell output power, The rate of change of radiation heat dissipation from the fuel cell to the surrounding environment, Forced heat dissipation change rate for the cooling system;

[0013] Step S12: Divide the battery stack into n parts in space, and regard each part as a node. The operating relationship between the heat generation power and the heat dissipation power of each node model can refer to formula (1). At the same time, the output parameters of the previous node model are regarded as the initial state parameters of the next node model.

[0014] Furthermore, the step S2 is specifically as follows:

[0015] Step S21: Select typical operating points of the rear section of the activation polarization region, the entire section of the ohmic polarization region, and the front section of the concentration polarization region, respectively, and use the system identification method to establish a prediction model set for the corresponding operating points. When the temperature MPC is running, the prediction model that matches the actual system working state is selected. Based on the prediction model, optimize the control and set the objective function:

[0016]

[0017] in, The model estimates the output of the system at time k+j based on current and historical input and output data; y r (k+j) is the expected output of the system at time k+j, Q and R are the weighting matrices of tracking error and control quantity respectively;

[0018] Step S22: Set the maximum temperature difference ΔT according to the actual parameters of the components of the fuel cell system max , upper temperature limit T up and the maximum temperature T max , to achieve temperature control.

[0019] Furthermore, the step S3 is specifically as follows:

[0020] Step S31: Construct an adaptive switching controller. The adaptive switching controller detects the temperature output by each node in real time and selects the area with the largest fuel cell deviation as the temperature for control feedback to improve management. The output temperature of each node is quantified using the following evaluation function:

[0021]

[0022] Among them, γ, μ, τ are all constants, l is an integer, e i (k) is the deviation between the output of the i-th node and the expected equilibrium value at time k:

[0023]

[0024] Step S32: The adaptive switching controller compares the evaluation functions in real time and selects the nth k The evaluation of the actual control state of each model is:

[0025]

[0026] Step S33: After determining the uniformity of all nodes using linear regression, the temperature values ​​of each model node are collected in real time, and the node with the largest deviation value is selected in real time in the entire area through the configuration of weight values ​​for correction control.

[0027] Furthermore, the configuration of the weight value adopts the principal component method to develop the weight coefficient, and the time when each node is selected as the control object, the position of the node, the real-time temperature of the node and the flow direction information of the coolant are used as reference information for the configuration of the weight value.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The present invention reduces the temperature difference inside the fuel cell and makes the temperature field distribution more uniform, thereby improving the output performance of the fuel cell;

[0030] 2. The present invention can enhance the stability of fuel cell output and reduce output ripple by homogenizing the temperature field;

[0031] 3. The present invention improves the temperature uniformity on the fuel cell plate, can promote the uniform distribution of the water content of the fuel cell membrane, make the output power more stable, and thus increase the service life of the fuel cell. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a control schematic diagram in an embodiment of the present invention;

[0033] Figure 2 The embodiment of the present invention measures the temperature at different locations of the fuel cell;

[0034] Figure 3 The temperature measurement results of the fuel cell at different positions after the multi-mode node adaptive control mentioned in the present invention is adopted in the embodiment of the present invention;

[0035] Figure 4 This is a comparison of the fuel cell output after the multi-mode node adaptive control mentioned in the present invention is adopted in the embodiment of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0037] Please refer to Figure 1 The present invention provides a thermal balance control method based on a fuel cell multi-mode node thermal model, comprising the following steps:

[0038] Step S1: Based on the spatial distribution of fuel cell temperature, a multi-mode node physical thermal model is constructed, including models of n nodes, forming a model library;

[0039] Step S2: Based on the multi-mode node physical thermal model, a linear method is used to design a corresponding model predictive controller to adjust the temperature of a specific area;

[0040] Step S3: Construct an adaptive switching controller, select the area with the largest temperature difference from other nodes in the model library in real time, perform correction control, and when the temperature of the area reaches the preset value, switch to the next model node for temperature correction, thereby reducing the temperature difference of the temperature field. Figure 3 , and then the fuel cell voltage is increased under the same output current. Figure 4 .

[0041] In this embodiment, step S1 is specifically as follows:

[0042] Step S11: The temperature model of the fuel cell is mainly determined by the fuel cell output power and the temperature control system. The temperature control system mainly consists of a circulating water pump, a thermostat, an electric three-way valve, a cooling fan, a water tank, and a temperature controller. The fuel cell characteristics are analyzed to obtain its working model. The operating relationship between the fuel cell heat generation power and the heat dissipation power is determined as follows:

[0043]

[0044] Where m fc is the total mass of the fuel cell, C fc is the specific heat capacity, T fc is the temperature of the fuel cell, H reac is the total heat generated by the fuel cell chemical reaction, P elec is the fuel cell output power, The fuel cell radiates heat to the surrounding environment. Force heat dissipation for the cooling system;

[0045] Step S12: Divide the battery stack into n parts in space, and regard each part as a node. The operating relationship between the heat generation power and the heat dissipation power of each node model can refer to formula (1). At the same time, the output parameters of the previous node model are regarded as the initial state parameters of the next node model.

[0046] In this embodiment, step S2 is specifically as follows:

[0047] Step S21: Model prediction has the advantage that the model is easy to obtain. Based on the obtained prediction model, optimization control is performed and the objective function is set:

[0048]

[0049] in, The model estimates the output of the system at time k+j based on current and historical input and output data; y r (k+j) is the expected output of the system at time k+j, Q and R are the weighting matrices of tracking error and control quantity respectively;

[0050] Step S22: According to the actual parameters of the system components, corresponding constraints are set to limit the maximum speed of components such as fans and water pumps, thereby indirectly controlling the heat dissipation and achieving temperature control.

[0051] In this embodiment, step S3 is specifically as follows:

[0052] The adaptive switching controller detects the output temperature of each node in real time and selects the area with the largest deviation in the fuel cell area as the control feedback temperature for improvement management. The output temperature of each node is quantified using the following evaluation function:

[0053]

[0054] Among them, γ, μ, τ are all constants, l is an integer, e i (k) is the deviation between the output of the i-th node and the expected equilibrium value at time k;

[0055]

[0056] The adaptive switching controller compares the evaluation function in real time and selects the nth k The evaluation of the actual control state of each model is:

[0057]

[0058] The principal component method is applied to develop the weight coefficient. The time when each node is selected as the control object, the node's location, the node's real-time temperature, the flow direction of the coolant and other information are used as reference information for the configuration of the weight value, thereby realizing the global switching of the node and further achieving the purpose of temperature balance management of the fuel cell stack.

[0059] The above description is only a preferred embodiment of the present invention. All equivalent changes and modifications made according to the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A thermal balance control method based on a fuel cell multi-mode node thermal model, characterized in that: The following steps are involved: Step S1: Based on the spatial distribution of fuel cell temperature, a multi-mode node physical thermal model is constructed, including models of n nodes, forming a model library; Step S2: Based on the multi-mode node physical thermal model, a linear method is used to design a corresponding model predictive controller to adjust the temperature of a specific area; Step S3: Construct an adaptive switching controller to select in real time the area in the model library with the largest temperature difference from other nodes and perform correction control. When the temperature of the area reaches the preset value, switch to the next model node for temperature correction, thereby reducing the temperature difference in the temperature field and increasing the fuel cell voltage under the condition of the same output current; The step S1 is specifically as follows: Step S11: Analyze the fuel cell characteristics, derive its working model, and determine the operating relationship between the fuel cell heat generation power and heat dissipation power as follows: Where m fc is the total mass of the fuel cell, C fc is the specific heat capacity, T fc is the temperature of the fuel cell, is the total heat change rate generated by the fuel cell chemical reaction, P elec is the fuel cell output power, The rate of change of radiation heat dissipation from the fuel cell to the surrounding environment, Forced heat dissipation change rate for the cooling system; Step S12: Divide the stack into n parts in space, and regard each part as a node. The operating relationship between the heat generation power and the heat dissipation power of each node model can be referred to formula (1). At the same time, the output parameters of the previous node model are regarded as the initial state parameters of the next node model. The step S2 is specifically as follows: Step S21: Select typical operating points of the rear section of the activation polarization region, the entire section of the ohmic polarization region, and the front section of the concentration polarization region, respectively, and use the system identification method to establish a prediction model set for the corresponding operating points. When the temperature MPC is running, the prediction model that matches the actual system working state is selected. Based on the prediction model, optimize the control and set the objective function: in, Nu is the prediction and control step size of the model predictive controller, The model estimates the output of the system at time k+j based on current and historical input and output data; y r (k+j) is the expected output of the system at time k+j, Q and R are the weighting matrices of tracking error and control quantity respectively; Step S22: Set the maximum temperature difference ΔT according to the actual parameters of the components of the fuel cell system max , upper temperature limit T up and the maximum temperature T max , to achieve temperature control; The step S3 is specifically as follows: Step S31: Construct an adaptive switching controller. The adaptive switching controller detects the temperature output by each node in real time and selects the area with the largest fuel cell deviation as the temperature for control feedback to improve management. The output temperature of each node is quantified using the following evaluation function: Among them, γ, μ, τ are all constants, l is an integer, e i (k) is the deviation between the output of the i-th node and the expected equilibrium value at time k: Step S32: The adaptive switching controller compares the evaluation functions in real time and selects the evaluation of the actual control state of the j'th model as: Step S33: After determining the uniformity of all nodes using linear regression, the temperature values ​​of each model node are collected in real time, and the node with the largest deviation value is selected in real time in the entire area through the configuration of weight values ​​for correction control.

2. The thermal balance control method based on the fuel cell multi-mode node thermal model according to claim 1 is characterized in that: The configuration of the weight value adopts the principal component analysis method to develop the weight coefficient, and the time when each node is selected as the control object, the position of the node, the real-time temperature of the node and the flow direction of the coolant are used as reference information for the configuration of the weight value.

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