Hydrogen fuel cell temperature control method based on neural network algorithm, control equipment and computer readable storage medium

Abstract

The invention relates to the technical field of fuel cell temperature control, in particular to a hydrogen fuel cell temperature control method and control equipment based on a neural network algorithm and a computer readable storage medium, and the method comprises the following steps: S1, constructing a neural network model; s2, working condition identification; s3, data acquisition; s4, neural network model operation; s5, temperature regulation and control; according to the method, the PID parameter value is adjusted only by the neural network model under the variable load working condition, the calculation power is small, and the robustness is high.

Description

Technical Field

[0001] The present invention relates to the field of fuel cell temperature control technology, and in particular to a hydrogen fuel cell temperature control method, control device and computer-readable storage medium based on a neural network algorithm. Background Art

[0002] A key factor in hydrogen fuel cell control is temperature, which directly affects the efficiency and stability of the fuel cell's output electrical performance. The more precise the temperature control, the more conducive it is to improving various performance indicators of hydrogen fuel cells.

[0003] The temperature control of hydrogen fuel cells mostly uses PID controllers. When the PID controller performs temperature control, different PID control quantities need to be calibrated. The whole process is relatively cumbersome and performs poorly in complex nonlinear hydrogen fuel cell systems. In order to overcome the shortcomings of the PID controller, the existing temperature control method uses a neural network model to adaptively adjust the PID control quantity. However, the neural network model does not distinguish the working conditions of the hydrogen fuel cell when adjusting the PID control quantity. Under steady-state conditions (i.e., non-variable load), the PID controller can continue to operate according to the corresponding PID control quantity without adjustment, resulting in excessive computing power, high energy consumption, and poor robustness of the PID controller. At the same time, in each hidden layer of the neural network model, the weights of the neurons in the current layer are not connected to the weights of the neurons in the previous layer, resulting in the weight values ​​of the neurons in the current layer being unreliable, which leads to the output results of the output layer being not accurate enough, and there is still room for improvement in robustness. Summary of the invention

[0004] The technical problem to be solved by the present invention is: to overcome the deficiencies in the prior art and to provide a hydrogen fuel cell temperature control method, control device and computer-readable storage medium based on a neural network algorithm, which adjusts the PID parameter value only under variable load conditions, has low computing power and strong robustness.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a hydrogen fuel cell temperature control method based on a neural network algorithm, comprising the following steps:

[0006] S1. Construction of neural network model: construct input layer, hidden layer and output layer, where the hidden layer is set to multiple layers, and the number of neurons in each hidden layer is staggered in sequence;

[0007] S2. Working condition identification: collect real-time power. If the real-time power is within the preset power range, it is non-variable load, and repeat step S2; otherwise, it is variable load, and execute step S3;

[0008] S3. Data acquisition: collect the previous PID parameter value and calculate the target temperature, where the target temperature is the average value of the current real-time temperature collected multiple times;

[0009] S4. Neural network model operation: input the acquired data into the neural network model, calculate the number of iterations of the neural network model, until the predetermined number of iterations is reached, and fix the PID parameter value;

[0010] S5. Temperature control: Use the fixed PID parameter value to perform PID control on the fan and return to step S2.

[0011] Furthermore, the calculation formula of the neurons in each hidden layer in step S1 is:

[0012]

[0013]

[0014] In the formula, Indicates Tier The value of a neuron, Represents the weight of the connection between the current layer of neurons and the previous layer of neurons, represents the number of neurons in the previous layer, Indicates Tier The correction matrix corresponding to the neurons, and The value range of each element in is 0-1. Indicates Tier The learning rate matrix corresponding to the neurons, and The value range of each element in is 0-1. represents the matrix weight of the previous neuron connection, Represents the average value of the weights connecting the current neuron and the previous neuron.

[0015] Furthermore, if , then the neuron value is discarded.

[0016] Furthermore, in step S1, there are 5 hidden layers, and the number of neurons in each hidden layer is 4, 5, 4, 5 and 4 respectively.

[0017] Furthermore, in step S2, the preset power range is 0.98P-1.02P, where P represents the current net power.

[0018] Furthermore, the number of times the current real-time temperature is collected in step S3 is 5 times.

[0019] Furthermore, the predetermined number of iterations in step S4 is 1000.

[0020] A control device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to implement the steps of the above control method.

[0021] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above control method.

[0022] The beneficial effects of the present invention are:

[0023] (1) The present invention identifies the working condition by presetting the power range. The neural network model adjusts the PID parameter value only under the variable load condition, thereby reducing the computing power and energy consumption and improving the robustness. At the same time, combined with the predetermined number of iterations, the neural network model iterations fix the PID parameter value when the predetermined number of iterations is reached, further reducing the computing power and energy consumption, thereby mitigating the temperature fluctuation under non-variable load and variable load conditions.

[0024] (2) The present invention links the current neuron weight with the neuron weight of the previous layer through the neuron calculation formula, using the slope and a fixed parameter matrix , In this way, the weight value of the neurons in the current layer is corrected, and the value of each neuron in the current layers is adjusted more quickly and accurately, which improves the reliability of the weight value of the neurons in the current layer, and then improves the accuracy of the output results of the output layer, thereby further improving the robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0026] Figure 1 is a flow chart of the control method of the present invention;

[0027] Figure 2 It is a structural diagram of the neural network model in the present invention;

[0028] Figure 3 It is the principle diagram of PID control in the present invention. DETAILED DESCRIPTION

[0029] The present invention will now be further described in conjunction with the accompanying drawings and preferred embodiments. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0030] Example 1

[0031] like Figure 1-Figure 3 As shown, a hydrogen fuel cell temperature control method based on a neural network algorithm comprises the following steps:

[0032] S1. Construction of neural network model: construct input layer, hidden layer and output layer, where the hidden layer is set to multiple layers, and the number of neurons in each hidden layer is arranged alternately in sequence.

[0033] Specifically, there are 5 hidden layers, and the number of neurons in each hidden layer is 4, 5, 4, 5 and 4 respectively. The number of neurons in each layer is arranged in a staggered manner, which is conducive to making the neural network better adapt to data and processing tasks, and also enables each layer to learn abstract representations of data at different levels; each layer in the neural network model is equipped with a different perceptron, which is composed of parameter weights, bias, and activation function. It can be forward propagated and backward propagated at the same time, and the parameter weights of the perceptron are adjusted. This is the prior art.

[0034] S2. Working condition identification: collect real-time power. If the real-time power is within the preset power range, it is non-variable load and step S2 is repeated; otherwise, it is variable load and step S3 is executed.

[0035] Specifically, the preset power range is 0.98P-1.02P, where P represents the current net power.

[0036] S3. Data acquisition: Collect the previous PID parameter value and calculate the target temperature, where the target temperature is the average value of the current real-time temperature collected multiple times.

[0037] Specifically, the current real-time temperature is collected 5 times; the PID parameters are parameter P, parameter I and parameter D.

[0038] S4. Neural network model operation: input the acquired data into the neural network model, calculate the number of iterations of the neural network model, until the predetermined number of iterations is reached, and fix the PID parameter value.

[0039] Specifically, the predetermined number of iterations is 1000.

[0040] S5. Temperature control: Use the fixed PID parameter value to perform PID control on the fan and return to step S2.

[0041] Specifically, Figure 3 The actuator refers to the fan speed.

[0042] The operating conditions are identified by pre-setting the power range, and the neural network model adjusts the PID parameter values ​​only under variable load conditions, which reduces computing power and energy consumption and improves robustness. At the same time, combined with the predetermined number of iterations, the PID parameter values ​​are fixed when the neural network model iterations reach the predetermined number of iterations, which further reduces computing power and energy consumption, thereby mitigating temperature fluctuations under non-variable load and variable load conditions.

[0043] The calculation formula for the neurons in each hidden layer in step S1 is:

[0044]

[0045]

[0046] In the formula, Indicates Tier The value of a neuron, Represents the weight of the connection between the current layer of neurons and the previous layer of neurons, represents the number of neurons in the previous layer, Indicates Tier The correction matrix corresponding to the neurons, and The value range of each element in is 0-1. Indicates Tier The learning rate matrix corresponding to the neurons, and The value range of each element in is 0-1. represents the matrix weight of the previous neuron connection, Represents the average value of the weights connecting the current neuron and the previous neuron.

[0047] The current neuron weight is linked to the neuron weight of the previous layer through the neuron calculation formula, using the slope and a fixed parameter matrix , In this way, the weight value of the current layer of neurons is corrected, and the value of each neuron in the current layer is adjusted more quickly and accurately, which improves the reliability of the weight value of the current layer of neurons, and then improves the accuracy of the output result of the output layer, thereby further improving the robustness. Among them, the correction matrix The degree to which the current neuron is affected by the previous neuron can be adjusted; the learning rate matrix The weight influence of the previous neuron can be added to the next neuron, controlling the step size of the bias update, avoiding skipping the optimal solution due to too fast an update or too long a learning time due to too slow an update, and finding a more appropriate value by pre-determining the number of iterations.

[0048] Specifically, if , then the neuron value is discarded.

[0049] Example 2

[0050] A control device includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to implement the steps of the control method described in Example 1.

[0051] Example 3

[0052] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the control method described in Example 1.

[0053] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with this technology to understand the content of the present invention and implement it. They cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A hydrogen fuel cell temperature control method based on a neural network algorithm, characterized in that: The steps include: S1. Construction of neural network model: construct input layer, hidden layer and output layer, where the hidden layer is set to multiple layers, and the number of neurons in each hidden layer is staggered in sequence; S2. Working condition identification: collect real-time power. If the real-time power is within the preset power range, it is non-variable load, and repeat step S2; otherwise, it is variable load, and execute step S3; S3. Data acquisition: collect the previous PID parameter value and calculate the target temperature, where the target temperature is the average value of the current real-time temperature collected multiple times; S4. Neural network model operation: input the acquired data into the neural network model, calculate the number of iterations of the neural network model, until the predetermined number of iterations is reached, and fix the PID parameter value; S5. Temperature control: Use the fixed PID parameter value to perform PID control on the fan and return to step S2.

2. The hydrogen fuel cell temperature control method based on the neural network algorithm according to claim 1 is characterized in that: The calculation formula of neurons in each hidden layer in step S1 is: In the formula, Indicates Tier The value of a neuron, Represents the weight of the connection between the current layer of neurons and the previous layer of neurons, represents the number of neurons in the previous layer, Indicates Tier The correction matrix corresponding to the neurons, and The value range of each element in is 0-1. Indicates Tier The learning rate matrix corresponding to the neurons, and The value range of each element in is 0-1. represents the matrix weight of the previous neuron connection, Represents the average value of the weights connecting the current neuron and the previous neuron.

3. The hydrogen fuel cell temperature control method based on the neural network algorithm according to claim 2 is characterized in that: like , then the neuron value is discarded.

4. The hydrogen fuel cell temperature control method based on the neural network algorithm according to claim 1 is characterized in that: In step S1, there are 5 hidden layers, and the number of neurons in each hidden layer is 4, 5, 4, 5 and 4 respectively.

5. The hydrogen fuel cell temperature control method based on the neural network algorithm according to claim 1 is characterized in that: The preset power range in step S2 is 0.98P-1.02P, where P represents the current net power.

6. The hydrogen fuel cell temperature control method based on the neural network algorithm according to claim 1 is characterized in that: The number of times the current real-time temperature is collected in step S3 is 5 times.

7. The hydrogen fuel cell temperature control method based on the neural network algorithm according to claim 1 is characterized in that: The predetermined number of iterations in step S4 is 1000.

8. A control device, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to implement the steps of any one of the control methods of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the control method according to any one of claims 1 to 7 are implemented.

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