Two-stage liquid cooling charging pile power module efficiency optimization method based on genetic algorithm

Abstract

The invention provides a two-stage liquid cooling charging pile power module efficiency optimization method based on a genetic algorithm. A preceding-stage sampling circuit, a backward-stage sampling circuit, serial port communication, the genetic algorithm and closed-loop control are included. The pre-stage sampling circuit is used for acquiring the pre-stage input voltage Uin, current Iin and temperature Temp1; the post-stage sampling circuit is used for acquiring the output voltage Uout, the current Iout and the temperature Temp2 of the post-stage LLC; and the serial port communication is used for transmitting sampling information of a rear stage to a front stage, acquiring an intermediate bus voltage reference value of efficiency and temperature optimization control through a genetic algorithm in a front stage digital control processor, and sending the intermediate bus voltage reference value to a front stage closed-loop controller. According to the two-stage liquid cooling charging pile power module efficiency optimization method based on the genetic algorithm provided by the invention, the genetic algorithm is used for optimizing each parameter, and the closed-loop controller is used for adjusting the voltage value of the intermediate bus in real time according to the external requirements and the change of the working environment, so that the optimal working state of the converter is achieved.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering, and in particular to a method for optimizing the efficiency of a two-stage liquid-cooled charging pile power module using a genetic algorithm. Background Art

[0002] With the rapid development of electric vehicles, the demand for charging piles is growing. Efficient and reliable charging piles are essential to promote the popularization of electric vehicles. In charging piles, the power module is one of the key components, and its efficiency directly affects the performance and operating cost of the entire charging pile.

[0003] The two-stage structure usually includes a front-stage power factor correction and a rear-stage DC / DC conversion. This structure can achieve input power factor correction, stable DC output and good electrical isolation.

[0004] The application of liquid cooling technology can effectively improve the heat dissipation performance of power modules, reduce module temperature, and improve reliability and life. However, the design and control of the liquid cooling system will also affect the efficiency of the power module.

[0005] Traditional power module efficiency optimization methods mainly focus on improving hardware design and control strategies, such as optimizing the selection of power devices, reducing switching losses, and improving the efficiency of magnetic components. However, these methods are often limited by hardware costs, technical difficulties, and performance limits.

[0006] The working environment of charging piles is complex and changeable, including different input voltages, output power requirements, and temperature changes, which makes it more difficult to optimize the efficiency of power modules.

[0007] Therefore, it is necessary to provide a two-stage liquid-cooled charging pile power module efficiency optimization method based on a genetic algorithm to solve the above technical problems. Summary of the invention

[0008] The present invention provides a two-stage liquid-cooled charging pile power module efficiency optimization method based on a genetic algorithm, which solves the problem that traditional power module efficiency optimization methods mainly focus on the improvement of hardware design and control strategies, such as optimizing the selection of power devices, reducing switching losses, and improving the efficiency of magnetic components. However, these methods are often limited by hardware costs, technical difficulties and performance limits.

[0009] In order to solve the above technical problems, the present invention provides a two-stage liquid-cooled charging pile power module efficiency optimization method using a genetic algorithm, comprising: Pre-sampling circuit, post-sampling circuit, serial communication, genetic algorithm and closed-loop control; The front-stage sampling circuit is used to obtain the input voltage Uin, current Iin and temperature Temp1 of the front-stage; The post-stage sampling circuit is used to obtain the output voltage Uout, current Iout and temperature Temp2 of the post-stage LLC; The serial communication is used to transmit the sampling information of the latter stage to the former stage, and the genetic algorithm inside the digital control processor of the former stage obtains the intermediate bus voltage reference value for the efficiency and temperature optimization control, and sends it to the closed-loop controller of the former stage; The genetic algorithm is used to optimize various parameters; The closed-loop controller is used to adjust the intermediate bus voltage value in real time according to external demand and changes in the working environment to achieve the optimal working state of the converter.

[0010] Preferably, the genetic algorithm is used to handle multi-objective optimization problems, reduce charging time, improve charging efficiency, and help design parameter combinations. Through genetic operations such as crossover, mutation, and selection, the algorithm gradually generates better parameter settings, thereby improving the adaptability and robustness of the charging system in different usage scenarios.

[0011] Preferably, the genetic algorithm two-stage liquid-cooled charging pile power module efficiency optimization method comprises the following steps: S1, obtain the input voltage Uin, current Iin, temperature Temp1 of the front stage, and the output voltage Uout, current Iout, temperature Temp2 of the rear stage LLC through the sampling circuit; S2, the sampling information of the latter stage is transmitted to the former stage through the serial communication, and the genetic algorithm inside the digital control processor of the former stage obtains the intermediate bus voltage reference value for the efficiency and temperature optimization control, and sends it to the closed-loop controller of the former stage; S3, the closed-loop controller adjusts the intermediate bus voltage value in real time according to external demand and changes in the working environment to achieve the optimal working state of the converter.

[0012] Preferably, the LLC resonant converter in S1 improves the efficiency of power conversion and achieves a smaller and lighter design.

[0013] Preferably, the LLC resonant converter in S1 can achieve high-efficiency power transmission within a wide load range and has low switching losses, can achieve zero voltage or zero current switching, reduces switch stress and losses, and improves system reliability and efficiency.

[0014] Preferably, the use of an LLC resonant converter in S1 helps to achieve a high-efficiency charging process, especially in the case of high-power fast charging, which can effectively reduce energy loss and reduce the heat generation of the charging pile, thereby improving the overall performance and service life of the charging pile.

[0015] Preferably, the genetic algorithm in S2 comprises the following steps: S21, firstly generate an initial population according to the input voltage Uin, the input current Iin, the output voltage Uout, the output current Iout and the input and output terminal temperatures Temp1 and Temp2; S22, then randomly select several groups of parameters that meet the input and output requirements for exchange and randomly modify a certain parameter after the exchange to introduce diversity and prevent the algorithm from falling into a local optimal solution; S23. Finally, the relationship between temperature and efficiency is calculated according to the evaluation function, and high efficiency is guaranteed under the premise of keeping the temperature as low as possible. This is used as the fitness evaluation principle to generate a roulette wheel to select the next generation of parameters.

[0016] Preferably, the genetic algorithm in S2 includes determining problems and parameters, encoding parameters, initializing populations, evaluating fitness, genetic operations, iterative optimization and structural analysis.

[0017] Preferably, the determining problem and parameters include clarifying the optimization problem and determining the parameters, the encoding parameters include selecting the encoding method and determining the encoding length, the initializing population includes randomly generating the initial population and determining the population size, and the evaluating fitness includes calculating the fitness function and selecting the fitness function.

[0018] Preferably, the genetic operation includes a selection operation, a crossover operation and a mutation operation, and the structural analysis includes decoding the optimal individual and result analysis.

[0019] Compared with the related art, the two-stage liquid-cooled charging pile power module efficiency optimization method provided by the genetic algorithm of the present invention has the following beneficial effects: The present invention provides a two-stage liquid-cooled charging pile power module efficiency optimization method using a genetic algorithm, which uses a front-stage sampling circuit to obtain the input voltage Uin, current Iin, and temperature Temp1 of the front stage, and a rear-stage sampling circuit to obtain the output voltage Uout, current Iout, and temperature Temp2 of the rear-stage LLC. The serial port communication is used to transmit the sampling information of the rear stage to the front stage. The genetic algorithm inside the front-stage digital control processor obtains the intermediate bus voltage reference value for efficiency and temperature optimization control, and sends it to the front-stage closed-loop controller. The genetic algorithm is used to optimize various parameters, and the closed-loop controller is used to adjust the intermediate bus voltage value in real time according to external requirements and changes in the working environment to achieve the optimal working state of the converter. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic structural diagram of a first embodiment of a two-stage liquid-cooled charging pile power module efficiency optimization method using a genetic algorithm provided by the present invention; Figure 2 for Figure 1 The schematic diagram of the genetic algorithm shown; Figure 3 Schematic diagram of the crossover mutation principle; Figure 4 A structural schematic diagram of a second embodiment of the two-stage liquid-cooled charging pile power module efficiency optimization method provided by the genetic algorithm of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0022] First embodiment Please refer to Figure 1 , Figure 2 and Figure 3 ,in, Figure 1 A schematic structural diagram of a first embodiment of a two-stage liquid-cooled charging pile power module efficiency optimization method using a genetic algorithm provided by the present invention; Figure 2 for Figure 1 The schematic diagram of the genetic algorithm shown; Figure 3 The schematic diagram of the crossover mutation principle. The two-stage liquid-cooled charging pile power module efficiency optimization method based on genetic algorithm includes: Pre-sampling circuit, post-sampling circuit, serial communication, genetic algorithm and closed-loop control; The front-stage sampling circuit is used to obtain the input voltage Uin, current Iin and temperature Temp1 of the front-stage; The post-stage sampling circuit is used to obtain the output voltage Uout, current Iout and temperature Temp2 of the post-stage LLC; The serial communication is used to transmit the sampling information of the latter stage to the former stage, and the genetic algorithm inside the digital control processor of the former stage obtains the intermediate bus voltage reference value for the efficiency and temperature optimization control, and sends it to the closed-loop controller of the former stage; The genetic algorithm is used to optimize various parameters; The closed-loop controller is used to adjust the intermediate bus voltage value in real time according to external demand and changes in the working environment to achieve the optimal working state of the converter.

[0023] The genetic algorithm is used to handle multi-objective optimization problems, reduce charging time, improve charging efficiency, and help design parameter combinations. Through genetic operations such as crossover, mutation, and selection, the algorithm gradually generates better parameter settings, thereby improving the adaptability and robustness of the charging system in different usage scenarios.

[0024] The genetic algorithm two-stage liquid-cooled charging pile power module efficiency optimization method comprises the following steps: S1, obtain the input voltage Uin, current Iin, temperature Temp1 of the front stage, and the output voltage Uout, current Iout, temperature Temp2 of the rear stage LLC through the sampling circuit; S2, the sampling information of the latter stage is transmitted to the former stage through the serial communication, and the genetic algorithm inside the digital control processor of the former stage obtains the intermediate bus voltage reference value for the efficiency and temperature optimization control, and sends it to the closed-loop controller of the former stage; S3, the closed-loop controller adjusts the intermediate bus voltage value in real time according to external demand and changes in the working environment to achieve the optimal working state of the converter.

[0025] The LLC resonant converter in S1 improves the efficiency of power conversion and achieves a smaller and lighter design.

[0026] The LLC resonant converter in S1 can achieve high-efficiency power transmission within a wide load range and has low switching losses. It can achieve zero voltage or zero current switching, reduce switch stress and losses, and improve system reliability and efficiency.

[0027] The use of LLC resonant converter in S1 helps to achieve a high-efficiency charging process, especially in the case of high-power fast charging, which can effectively reduce energy loss and reduce the heat of the charging pile, thereby improving the overall performance and service life of the charging pile.

[0028] The genetic algorithm in S2 comprises the following steps: S21, firstly generate an initial population according to the input voltage Uin, the input current Iin, the output voltage Uout, the output current Iout and the input and output terminal temperatures Temp1 and Temp2; S22, then randomly select several groups of parameters that meet the input and output requirements for exchange and randomly modify a certain parameter after the exchange to introduce diversity and prevent the algorithm from falling into a local optimal solution; S23. Finally, the relationship between temperature and efficiency is calculated according to the evaluation function, and high efficiency is guaranteed under the premise of keeping the temperature as low as possible. This is used as the fitness evaluation principle to generate a roulette wheel to select the next generation of parameters.

[0029] Compared with the related art, the two-stage liquid-cooled charging pile power module efficiency optimization method provided by the genetic algorithm of the present invention has the following beneficial effects: The present invention provides a two-stage liquid-cooled charging pile power module efficiency optimization method using a genetic algorithm, which uses a front-stage sampling circuit to obtain the input voltage Uin, current Iin, and temperature Temp1 of the front stage, and a rear-stage sampling circuit to obtain the output voltage Uout, current Iout, and temperature Temp2 of the rear-stage LLC. The serial port communication is used to transmit the sampling information of the rear stage to the front stage. The genetic algorithm inside the front-stage digital control processor obtains the intermediate bus voltage reference value for efficiency and temperature optimization control, and sends it to the front-stage closed-loop controller. The genetic algorithm is used to optimize various parameters, and the closed-loop controller is used to adjust the intermediate bus voltage value in real time according to external requirements and changes in the working environment to achieve the optimal working state of the converter.

[0030] Second embodiment Please refer to Figure 4 Based on the two-stage liquid-cooled charging pile power module efficiency optimization method provided by the genetic algorithm in the first embodiment of the present application, the second embodiment of the present application proposes another two-stage liquid-cooled charging pile power module efficiency optimization method using a genetic algorithm. The second embodiment is only a preferred method of the first embodiment, and the implementation of the second embodiment will not affect the independent implementation of the first embodiment.

[0031] Specifically, the difference between the two-stage liquid-cooled charging pile power module efficiency optimization method provided by the genetic algorithm in the second embodiment of the present application is that the two-stage liquid-cooled charging pile power module efficiency optimization method provided by the genetic algorithm, the genetic algorithm in S2 includes determining problems and parameters, encoding parameters, initializing the population, evaluating fitness, genetic operations, iterative optimization and structural analysis.

[0032] The determining problem and parameters includes clarifying the optimization problem and determining the parameters, the encoding parameters include selecting the encoding method and determining the encoding length, the initializing population includes randomly generating the initial population and determining the population size, and the evaluating fitness includes calculating the fitness function and selecting the fitness function.

[0033] The genetic operation includes selection operation, crossover operation and mutation operation, and the structural analysis includes decoding the best individual and result analysis.

[0034] Identify the problem and parameters Define the optimization problem: determine the objective function and constraints to be optimized, for example, maximizing or minimizing a performance indicator while satisfying certain restrictions.

[0035] Determine parameters: Determine the parameters to be optimized and their ranges. These parameters can be real numbers, integers, or discrete values, depending on the nature of the problem.

[0036] 2. Encoding parameters Select the encoding method: Encode the parameters into a form that the genetic algorithm can process. Common encoding methods include binary encoding, real number encoding, and integer encoding.

[0037] Determine the encoding length: Determine the encoding length based on the parameter value range and accuracy requirements. The longer the encoding length, the higher the accuracy, but the amount of calculation will also increase.

[0038] 3. Initialize the population Randomly generate the initial population: Within the range of parameter values, a certain number of individuals are randomly generated as the initial population. The individuals can be encoded parameter values ​​or combinations of parameters.

[0039] Determine the population size: Determine the appropriate population size based on the complexity of the problem and the limitations of computing resources. The larger the population size, the larger the search space, but the computing time will also increase.

[0040] 4. Evaluating fitness Calculate the fitness function: For each individual, calculate its fitness value based on the objective function and constraints. The fitness value reflects the degree of excellence of the individual in the optimization problem.

[0041] Choosing a fitness function: The choice of fitness function should be consistent with the goal of the optimization problem. For example, if the goal is to maximize a certain performance metric, the fitness function can be a function value of that metric.

[0042] 5. Genetic Operation Selection operation: select a part of individuals from the current population as parents to generate the next generation of individuals. The selection operation can be performed based on the fitness value, and individuals with higher fitness are selected as parents. Common selection methods include roulette selection, tournament selection, etc.

[0043] Crossover operation: Perform a crossover operation on the selected parent individuals to generate new offspring individuals. The crossover operation can combine some genes of the parent individuals to form new individuals. Common crossover methods include single-point crossover and two-point crossover.

[0044] Mutation operation: Perform mutation operation on newly generated offspring individuals to introduce a certain degree of randomness and increase the diversity of the population. The mutation operation can randomly change certain gene values ​​of individuals. Common mutation methods include bit mutation and uniform mutation.

[0045] 6. Iterative Optimization Generate the next generation population: add the offspring individuals generated by genetic operations to the current population to form a new generation population.

[0046] Repeat steps 4 and 5: perform fitness evaluation and genetic operations on the new generation of population, and continuously iterate and optimize until the stopping condition is met.

[0047] Stopping conditions: You can set appropriate stopping conditions based on the requirements of the problem and the limitations of computing resources, such as reaching a certain number of iterations, the fitness value reaching a certain threshold, or converging to a certain degree.

[0048] 7. Results Analysis Decoding the optimal individual: When the genetic algorithm stops running, the individual with the highest fitness is selected as the optimal solution, and the encoding of the optimal individual is decoded into the actual parameter value.

[0049] Analyze the results: Analyze the optimal solution to evaluate its performance and feasibility in the optimization problem. If necessary, further adjust the parameters or improve the settings of the genetic algorithm and perform multiple optimization experiments to obtain better results.

[0050] Compared with the related art, the two-stage liquid-cooled charging pile power module efficiency optimization method provided by the genetic algorithm of the present invention has the following beneficial effects: The present invention provides a two-stage liquid-cooled charging pile power module efficiency optimization method using a genetic algorithm. In the genetic algorithm, by determining problems and parameters, encoding parameters, initializing populations, evaluating fitness, genetic operations, iterative optimization and structural analysis, the specific problems to be solved can be clearly defined, and a clear goal can be provided for subsequent optimization work, thereby avoiding blind searches, improving optimization efficiency and analyzing optimization results to verify the effectiveness and optimization effect of the algorithm. By comparing indicators such as the power module efficiency before and after optimization, it can be determined whether the algorithm has successfully improved system performance.

[0051] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A two-stage liquid-cooled charging pile power module efficiency optimization method based on a genetic algorithm, characterized in that: include: Pre-sampling circuit, post-sampling circuit, serial communication, genetic algorithm and closed-loop control; The front-stage sampling circuit is used to obtain the input voltage Uin, current Iin and temperature Temp1 of the front-stage; The post-stage sampling circuit is used to obtain the output voltage Uout, current Iout and temperature Temp2 of the post-stage LLC; The serial communication is used to transmit the sampling information of the latter stage to the former stage, and the genetic algorithm inside the digital control processor of the former stage obtains the intermediate bus voltage reference value for the efficiency and temperature optimization control, and sends it to the closed-loop controller of the former stage; The genetic algorithm is used to optimize various parameters; The closed-loop controller is used to adjust the intermediate bus voltage value in real time according to external demand and changes in the working environment to achieve the optimal working state of the converter.

2. The two-stage liquid-cooled charging pile power module efficiency optimization method based on genetic algorithm according to claim 1 is characterized in that: The genetic algorithm is used to deal with multi-objective optimization problems, reduce charging time, improve charging efficiency, and help design parameter combinations. Through genetic operations such as crossover, mutation, and selection, the algorithm gradually generates better parameter settings, thereby improving the adaptability and robustness of the charging system in different usage scenarios.

3. The two-stage liquid-cooled charging pile power module efficiency optimization method of genetic algorithm according to claims 1-2, characterized in that: The following steps are involved: S1, obtain the input voltage Uin, current Iin, temperature Temp1 of the front stage, and the output voltage Uout, current Iout, temperature Temp2 of the rear stage LLC through the sampling circuit; S2, the sampling information of the latter stage is transmitted to the former stage through the serial communication, and the genetic algorithm inside the digital control processor of the former stage obtains the intermediate bus voltage reference value for the efficiency and temperature optimization control, and sends it to the closed-loop controller of the former stage; S3, the closed-loop controller adjusts the intermediate bus voltage value in real time according to external demand and changes in the working environment to achieve the optimal working state of the converter.

4. The two-stage liquid-cooled charging pile power module efficiency optimization method based on genetic algorithm according to claim 3 is characterized in that: The LLC resonant converter in S1 improves the efficiency of power conversion and achieves a smaller and lighter design.

5. The two-stage liquid-cooled charging pile power module efficiency optimization method based on genetic algorithm according to claim 3 is characterized in that: The LLC resonant converter in S1 can achieve high-efficiency power transmission within a wide load range and has low switching losses. It can achieve zero voltage or zero current switching, reduce switch stress and losses, and improve system reliability and efficiency.

6. The two-stage liquid-cooled charging pile power module efficiency optimization method based on genetic algorithm according to claim 3 is characterized in that: The use of LLC resonant converter in S1 helps to achieve a high-efficiency charging process, especially in the case of high-power fast charging, which can effectively reduce energy loss and reduce the heat of the charging pile, thereby improving the overall performance and service life of the charging pile.

7. The two-stage liquid-cooled charging pile power module efficiency optimization method based on genetic algorithm according to claim 3 is characterized in that: The genetic algorithm in S2 comprises the following steps: S21, firstly generate an initial population according to the input voltage Uin, the input current Iin, the output voltage Uout, the output current Iout and the input and output terminal temperatures Temp1 and Temp2; S22, then randomly select several groups of parameters that meet the input and output requirements for exchange and randomly modify a certain parameter after the exchange to introduce diversity and prevent the algorithm from falling into a local optimal solution; S23. Finally, the relationship between temperature and efficiency is calculated according to the evaluation function, and high efficiency is guaranteed under the premise of keeping the temperature as low as possible. This is used as the fitness evaluation principle to generate a roulette wheel to select the next generation of parameters.

8. The two-stage liquid-cooled charging pile power module efficiency optimization method based on genetic algorithm according to claim 3 is characterized in that: The genetic algorithm in S2 includes determining problems and parameters, encoding parameters, initializing populations, evaluating fitness, genetic operations, iterative optimization and structural analysis.

9. The two-stage liquid-cooled charging pile power module efficiency optimization method based on genetic algorithm according to claim 8, characterized in that: The determining problem and parameters includes clarifying the optimization problem and determining the parameters, the encoding parameters include selecting the encoding method and determining the encoding length, the initializing population includes randomly generating the initial population and determining the population size, and the evaluating fitness includes calculating the fitness function and selecting the fitness function.

10. The two-stage liquid-cooled charging pile power module efficiency optimization method based on genetic algorithm according to claim 8, characterized in that: The genetic operation includes selection operation, crossover operation and mutation operation, and the structural analysis includes decoding the best individual and result analysis.

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