CN110348037B - Optimization method of electrical topological structure of automobile exhaust thermoelectric conversion device - Google Patents
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Abstract
The invention provides an optimization method of an electric topological structure of an automobile exhaust thermoelectric conversion device, which comprises the steps of testing the output performance of a single thermoelectric module, and establishing and constructing an equivalent circuit model of each thermoelectric module in the automobile exhaust thermoelectric conversion device; and establishing an expanded MTSP model according to the equivalent circuit model, performing optimal calculation on the topological structure by using a genetic algorithm to obtain an optimized MTSP model, obtaining the optimal structural unit number and the optimal topological structure, and constructing the automobile exhaust thermoelectric conversion device. After the topological structure of the automobile tail gas thermoelectric conversion device is optimized by combining the respective output characteristics, each structural unit is ensured to have larger open-circuit voltage and smaller internal resistance, so that the whole automobile tail gas thermoelectric conversion device can obtain larger peak power after the structural units are connected in series, and the whole efficiency and performance of the automobile tail gas thermoelectric conversion device are greatly improved.
Description
Technical Field
The invention belongs to the technical field of renewable energy sources, and particularly relates to an optimization method of an electrical topological structure of an automobile exhaust thermoelectric conversion device.
Background
Only about 30% of the fuel energy of a traditional internal combustion engine is converted into mechanical energy, and the rest is directly discharged in a cooling water or tail gas mode. If the waste heat discharged by the tail gas is recycled to generate electricity and is utilized in a vehicle-mounted system based on the thermoelectric conversion technology, the method has important significance for improving the fuel economy of an automobile engine. The method is a new technical approach for realizing power generation by recycling waste heat of automobile exhaust. The high-power automobile exhaust thermoelectric conversion device usually comprises dozens or hundreds of thermoelectric modules, and because of the influence of the structural design factors of an internal flow field, the surface temperature of the exhaust flowing through a heat exchanger is difficult to realize complete uniform distribution, so the heat source temperature of each thermoelectric module is different, and the temperature difference of the cold end and the hot end of each thermoelectric module is different under the same cold source condition. In addition, due to the limitations of the manufacturing process level and the inconsistency of the installation clamping manner, the internal resistances of the thermoelectric modules may also be greatly different. In actual output, if all the thermoelectric modules are connected in series, although the open-circuit voltage of the automobile exhaust thermoelectric conversion device is high, the internal resistance of the automobile exhaust thermoelectric conversion device is also large; if all the thermoelectric modules are randomly connected in parallel, although a large current can be output, circulation currents can be generated among the thermoelectric modules with different open-circuit voltage levels, so that power consumption in the automobile exhaust thermoelectric conversion device is caused, and the performance of the automobile exhaust thermoelectric conversion device is reduced.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the optimization method of the electrical topological structure of the automobile exhaust thermoelectric conversion device is provided, and the overall efficiency and performance of the automobile exhaust thermoelectric conversion device can be improved.
The technical scheme adopted by the invention for solving the technical problems is as follows: an optimization method of an electrical topological structure of an automobile exhaust thermoelectric conversion device is characterized by comprising the following steps: it comprises the following steps:
s1, testing the output performance of a single thermoelectric module, and creating and constructing an equivalent circuit model of each thermoelectric module in the automobile exhaust thermoelectric conversion device;
s2, coding and dividing thermoelectric modules into a plurality of structural units, establishing an expanded MTSP model according to the equivalent circuit model, and performing optimal calculation on a topological structure by using a genetic algorithm to obtain an optimized MTSP model;
s3, obtaining the optimal structural unit number and the optimal topological structure according to the optimized MTSP model;
and S4, constructing the automobile exhaust thermoelectric conversion device according to the optimal topological structure.
According to the above method, the S1 specifically includes:
1.1, maintaining constant cold source temperature and installation pressure;
1.2, measuring a group of voltage-current-power characteristic curves of a single thermoelectric module under different output current conditions;
1.3, changing the temperature of a heat source, and repeating 1.2;
and 1.4, according to the obtained voltage-current-power characteristic curve of the single thermoelectric module at different temperatures, equating the single thermoelectric module to a variable voltage source related to temperature difference and connecting a constant internal resistance in series, and establishing an equivalent circuit model.
According to the above method, the S2 specifically includes:
2.1, coding the thermoelectric module and dividing the thermoelectric module into a plurality of structural units;
2.2, dividing the thermoelectric modules in each structural unit, and calculating an unbalance factor to obtain an optimal division scheme;
2.3, generating an initial population and calculating the fitness;
2.4, sequentially carrying out selection, crossing and variation operation on the population;
2.5, judging whether a convergence condition is met, if the convergence condition is not met, returning to the step 2.4, and if the convergence condition is met, executing the step 2.6;
and 2.6, decoding to obtain an optimal solution, namely the optimized MTSP model.
According to the above method, the S3 specifically includes:
3.1, setting a plurality of groups of structural units;
3.2, sequentially substituting into S2, and obtaining corresponding optimal power values under different structural unit numbers by using a genetic algorithm;
and 3.3, carrying out cubic spline interpolation on the optimal power value obtained in the step 3.2 to obtain optimal peak power, wherein the number of the structural units corresponding to the optimal peak power and the combination of the thermoelectric modules in each structural unit are the optimal structural unit number and the optimal topological structure.
The invention has the beneficial effects that: after the topological structure of the automobile tail gas thermoelectric conversion device is optimized by combining the respective output characteristics, each structural unit is ensured to have larger open-circuit voltage and smaller internal resistance, so that the structural units can obtain larger peak power integrally after being connected in series, and the overall efficiency and performance of the automobile tail gas thermoelectric conversion device are greatly improved.
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FIG. 1 is a flowchart of a method according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of the layout and numbering of the thermoelectric module set according to the present invention.
Fig. 3 is a structural view of the automobile exhaust thermoelectric conversion device of the present invention.
Fig. 4 is a voltammogram of a single thermoelectric module of the present invention at different heat source temperatures with a heat sink temperature of 50 c.
Fig. 5 is a schematic diagram of an equivalent circuit model of a single thermoelectric module of the present invention.
FIG. 6 is a schematic of open circuit voltage of the thermoelectric module under various engine operating conditions in accordance with the present invention.
Fig. 7 is a graph of peak power versus current for a thermoelectric module of the present invention.
Figure 8 is a graph of the average internal resistance of a thermoelectric module of the present invention.
FIG. 9 is a graph of the optimum power level for the number of groups of structural units in example 11 of the present invention.
FIG. 10 is a graph showing the results of five iterations when the number of structural units is 12.
FIG. 11 is a graph of imbalance factor versus power after five optimizations in accordance with an embodiment of the present invention.
FIG. 12 is a graph comparing the results of the inventive algorithm with a progressive optimization algorithm based on a greedy idea.
Detailed Description
The invention is further illustrated by the following specific examples and figures.
The invention provides an optimization method of an electrical topological structure of an automobile exhaust thermoelectric conversion device, which comprises the following steps as shown in figure 1:
and S1, testing the output performance of the single thermoelectric module, and creating and constructing an equivalent circuit model of each thermoelectric module in the automobile exhaust thermoelectric conversion device.
S1 specifically includes the following substeps:
(1-1) maintaining constant cold source temperature and installation pressure: the temperature of the cold source is stabilized at 50 ℃ and the installation pressure is 30kg (corresponding to the pressure of 0.82 Bar). In order to describe the invention more intuitively, the structure diagram of the automobile exhaust thermoelectric conversion device is shown in the attached figure 3, the layout of thermoelectric modules is shown in the attached figure 2, and experimental cooperation explanation is carried out;
(1-2) measuring a group of typical voltage-current-power (V-I-P) characteristic curves of a single thermoelectric module under different output current conditions; FIG. 4 is a voltammogram of a single thermoelectric module at different temperatures of the heat source when the temperature of the heat sink is 50 ℃;
(1-3) changing the temperature of the heat source, wherein the temperature of the heat source is 230 ℃, 270 ℃, 310 ℃ and 350 ℃, and repeating (1-2) when the temperature of the heat source is changed;
(1-4) it can be seen from fig. 4 that the slope of the voltage-current characteristic curve of a single thermoelectric module remains substantially constant under different temperature differences, i.e. the internal resistance thereof can be considered to remain constant. Therefore, a single thermoelectric module can be equivalently connected with a variable voltage source related to temperature difference in series to form a constant internal resistance, and an equivalent model is established as shown in the figure 5.
The internal resistance r can be expressed as follows:
r＝U/Imax (1)
wherein Imax is a short-circuit current (a) of the thermoelectric module, and U is an open-circuit voltage;
the open-circuit voltage and the maximum peak power corresponding current of each thermoelectric module are tested under different engine working conditions by combining the adjustable electronic load under the experimental conditions (1-1) and (1-2) respectively shown in the attached figures 6 and 7. This provides the following references for series connection of thermoelectric modules of different voltage and current levels: the thermoelectric modules with close open-circuit voltage and internal resistance and lower peak power corresponding current are connected in parallel, and the thermoelectric modules with smaller peak power corresponding current and lower internal resistance are connected in series.
Calculating the average internal resistance estimate of each thermoelectric module using equation (1) based on the equivalent circuit model shown in fig. 5 as shown in fig. 8, it can be seen that the average internal resistances of the respective thermoelectric modules are different due to the differences in the process design and the bearing installation pressure of the respective thermoelectric modules, and are not directly linked to their position distribution on the heat exchanger surface.
S2, coding and dividing the thermoelectric modules into a plurality of structural units, establishing an expanded MTSP model according to the equivalent circuit model, and performing optimal calculation of a topological structure by using a genetic algorithm to obtain an optimized MTSP model.
S2 specifically includes the following substeps:
and (2-1) adopting decimal coding, coding L thermoelectric modules (L cities), dividing the L thermoelectric modules into M structural units (M travelers), and adding M-1 virtual symbols on the basis of the original city to represent the M-1 virtual city. The problem is simplified because each carrier does not have a fixed starting city and ending city in the problem studied by the present invention. This is illustrated on a smaller scale.
For example, there are 10 thermoelectric modules, numbered 1-10, with 3 structural units. One chromosomal code for MTSP is:
the thermoelectric module numbers assigned to the 3 structural units are:
*-8-4-* *5-9-6-3-10-* *-1-7-2-*
in the operation process of the algorithm, when the virtual symbols occur at two ends of a dyeing or adjacent to the virtual symbols, the chromosome cannot maintain the set number of the structural units, so the chromosome is naturally eliminated.
(2-2) setting a certain structural unit to have N thermoelectric modules, wherein each thermoelectric module consists of a voltage source and an internal resistance, and the step (2-1) knows that the thermoelectric modules are arranged according to a certain sequence. At this time, the voltage source set U is { U1, U2,. Un }, and the internal resistance set R is { R1, R2,. Rn }. At this time, our task is to divide it appropriately, including two aspects: 1. dividing the number of branches; 2. the position of the splitting branch.
After the thermoelectric modules of the branches are matched with each other, the parameters of each branch are as consistent as possible, at the moment, the open-circuit voltage of each structural unit is as large as the open-circuit voltage of one branch, and the internal resistance of the structural unit is changed into 1/s of that of a single branch. That is, when all modules have the same parameters and operate under the same conditions, the characteristics of the structural unit are the same as the parallel characteristics of the general dc power.
In order to reduce the space complexity of operation and reduce the negative power of circulation, the number of parallel branches of the structural unit is set
(in particular, when N is 1, S is 0, without dividing). Let the i-th branch split at the Ti-th (1 ≤ i)<s-1) thermoelectric modules, then a better slicing should be such that the imbalance factor Q is as small as possible, where Q is defined as:
generally, in a series of thermoelectric modules, although there are differences in open-circuit voltage and internal resistance, the overall fluctuation range is not too large, and therefore, from the viewpoint of equalization, there are:
for example, the following table sets forth the parameters of the thermoelectric modules
The structural unit has 5 thermoelectric modules, so that the structural unit is only segmented once, and the value of T can be 1, 2,3 and 4.
As can be seen from the table, when Q is smaller, the corresponding peak power is larger. And the optimum division also satisfies the formula (3). Therefore, the encoding for the structural unit is only 2,3 during the calculation, and 3 is finally preferred after the calculation. At this time, the peak power of the structural unit was 4.725w, which was 97.51% of the reference power (the sum of the peak powers of the thermoelectric modules).
(2-3) generating an initial population, wherein the number of the population is generally determined according to the size of the urban scale, the value of the population is floated between 50 and 200, and the fitness is calculated as follows:
the fitness function is the peak power corresponding to the optimal thermoelectric module topological structure, the optimization aim is to select the chromosome with the fitness function value as large as possible, and the chromosome with the fitness function value larger is better, and vice versa.
(2-4) firstly, carrying out selection operation on the population, adopting linear ranking selection, and ranking from good to bad according to the fitness of each chromosome obtained in the step (2-3), wherein the selection probability is as follows:
Pi(a-b/(n +1))/n (where n is rank, a 1.1, b 2a-2)
Then, a crossover operation is performed, which means that two chromosomes paired with each other exchange genes in some way to form two new individuals. In genetic algorithms, cross-over operations are important means to obtain good individuals, and determine the global search capability of the GA. Partial mapping hybridization is used herein to determine the parents of the crossover operation, and the parent samples are grouped pairwise, with the following process repeated for each group (assuming a city number of 10).
(1) Two random integers r1 and r2 in the interval of [ 1,10 ] are generated, two positions are determined, and data between the two positions are crossed, wherein r1 is equal to 4, and r2 is equal to 7.
The intersection is as follows:
(2) after crossing, the same individual has repeated city number, non-repeated number is retained, and the number with conflict (band) adopts partial mapping method to eliminate conflict, i.e. uses the corresponding relation of middle section to make mapping. The result is that
Finally, carrying out mutation operation, wherein the mutation operation is to replace the gene values on certain loci of the individual chromosome coding string with other alleles on the loci so as to form a new individual. Mutation is also a method for generating new individuals, and more importantly, it determines the capacity of GA local search.
Therefore, in order to improve the local search capability of the GA, three operations of interchange, inversion and translation are adopted herein. Generating two random integers r1 and r2 between [ 1 and 10 ], determining two positions, and carrying out mutation operation. For example, r 1-4 and r 2-7 (the same applies below). Let the original sequence be 95173861042, then
(1) After interchanging: 95163871042
(2) After inversion: 95168371042
(3) After translation: 95138671042
(2-5) judging whether a convergence condition is met, if the set iteration times are met, returning to the step (2-4) if the set iteration times are not met, and executing the step (2-6) if the set iteration times are met;
and (2-6) decoding and outputting the currently optimized MTSP model.
And S3, obtaining the optimal structural unit number and the optimal topological structure according to the optimized MTSP model.
S3 specifically includes the following substeps:
(3-1) setting a plurality of groups of structural units such as 1,10, 20, 30, 40, 50, 60, 70, 80, 90, 100 and the like;
(3-2) sequentially substituting the optimized MTSP model, and respectively iterating 800 times by using the genetic algorithm of S2 to obtain the optimal power value under each structural unit number, as shown in FIG. 9;
and (3-3) carrying out cubic spline interpolation on the optimal power value obtained in the step (3-3), and finding that when the number of the structural units is 12, the optimal peak power can be obtained, and the number of the structural units corresponding to the optimal peak power and the combination of the thermoelectric modules in each structural unit are the optimal structural unit number and the optimal topological structure.
And S4, constructing the automobile exhaust thermoelectric conversion device according to the optimal topological structure.
As can be seen from the step (3-3), the number of the current optimal structural units is 12, and the algorithm parameters are unchanged. To characterize the genetic algorithm, we increased the number of iterations to 800 and performed 5 times, noting the optimal power value for each generation and comparing it to the series, parallel and reference powers, see fig. 10.
As shown in fig. 10, in the initial stage of iteration, the optimal power increases rapidly, and the method conforms to the characteristic of fast convergence of the genetic algorithm. After 400 generations, the value tends to be stable, with an optimum power of up to 42.6767w, representing 99.6% of the reference power. Compared with a pure series connection mode, the method is improved by 12.56 percent, and compared with a series-parallel connection mode, the method is improved by 13.82 percent.
Meanwhile, five iteration curves are compared, and the rising areas of the five iteration curves before the generation 100 are found to be quite identical, so that the algorithm is strong in global searching capability. But the transition region between generations 100 and 300 is more diverse because of the larger scale and internal structural processing herein. After 400 generations, each curve has a certain convergence, which indicates that the global optimizing capability of the algorithm has better stability.
Since we introduce this parameter as an imbalance factor when internally sectioning the building block. While the same is less optimal, the comparison of different structural units is not meaningful due to the difference in the number of thermoelectric modules. However, each time optimization is performed, the overall unbalance factor can reflect the unbalance degree of the structure. Next, using the structure generated after the five optimizations, the sum of the imbalance factors of the respective structural units is calculated, and the relationship with the optimized power is analyzed, as shown in fig. 11. It can be seen that the larger the sum of the imbalance factors of a structure, the lower its power. However, an anomaly is also shown in which the fourth imbalance factor is less than the fifth imbalance factor, but has a higher power than the fifth imbalance factor. This is because the imbalance factor defined herein reflects the sum of the voltage and internal resistance fluctuations of the branches of the structural unit, and is an equal weighted sum of the two. In practice, the degree of imbalance of a structure is not only related to the open circuit voltage and the internal resistance. This document is only in terms of its main determinants, but generally satisfies the rules. This further demonstrates the effectiveness of the model.
To show the superiority of the algorithm of the present invention, we designed a comparison with a greedy thought based progressive optimization algorithm, and the result is shown in fig. 12. From the optimization process of the same problem, the step-by-step optimization algorithm is a local optimization algorithm, and although the increase is higher in a certain interval, the convergence trend is not obvious on the whole. In contrast, the genetic algorithm adopted by the invention has the advantages of fast early growth, slow late growth, obvious convergence trend and capability of exceeding the step-by-step iterative algorithm when the execution reaches 150 generations.
In addition, the step-by-step optimization is based on a greedy criterion, the step-by-step optimization does not have global optimization capability, and the genetic algorithm can increase the population scale and the iteration times to enable the optimization structure to be close to global optimization as much as possible.
However, in terms of execution efficiency, the genetic algorithm is generally inferior to the greedy algorithm, but as the problem scale is enlarged, the deviation between the optimization result of the traditional greedy algorithm and the global optimum is larger and larger, and at this time, the superiority of the group intelligence algorithm is shown.
Because the temperature distribution everywhere on the heat exchanger surface of car tail gas thermoelectric conversion device is difficult to realize the exact same, can cause the heat source temperature of each thermoelectric module like this to have a height to have a low, when adopting the outside cooling system of current list formula cold source structure, the difference in temperature of each thermoelectric module has certain difference, thereby cause the maximum output current that open circuit voltage and peak power correspond all can be different, plus the nuance of original manufacturing process and mounting means, the self characteristic of each thermoelectric module, if: the parameters such as internal resistance are different. If all the thermoelectric modules are connected in series, the maximum internal resistance of the system is also maximum (the sum of the internal resistances of all the thermoelectric modules), and the maximum output current of the system is smaller and is influenced by the thermoelectric modules with smaller temperature difference due to the barrel effect; if thermoelectric modules with different output characteristics are randomly connected in parallel, because the final output end voltages are required to be kept consistent, according to kirchhoff's law, certain circulation currents exist among the thermoelectric modules with different open-circuit voltages, and the internal resistance flowing through the thermoelectric modules can generate certain heat, so that the internal power consumption of the system is increased, the output performance of the system is reduced, and the power generation performance of each thermoelectric module is difficult to exert under both conditions. After the topological structure of the automobile tail gas thermoelectric conversion device is optimized by combining the respective output characteristics, each structural unit is ensured to have larger open-circuit voltage and smaller internal resistance, so that the whole automobile tail gas thermoelectric conversion device can obtain larger peak power after the structural units are connected in series, and the whole efficiency and performance of the automobile tail gas thermoelectric conversion device are greatly improved.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (2)
1. An optimization method of an electrical topological structure of an automobile exhaust thermoelectric conversion device is characterized by comprising the following steps: it comprises the following steps:
s1, testing the output performance of a single thermoelectric module, and creating and constructing an equivalent circuit model of each thermoelectric module in the automobile exhaust thermoelectric conversion device;
s2, coding and dividing thermoelectric modules into a plurality of structural units, establishing an expanded MTSP model according to the equivalent circuit model, and performing optimal calculation on a topological structure by using a genetic algorithm to obtain an optimized MTSP model;
s2 specifically includes:
2.1, coding the thermoelectric module and dividing the thermoelectric module into a plurality of structural units;
2.2, dividing the thermoelectric modules in each structural unit, and calculating an unbalance factor to obtain an optimal division scheme;
2.3, generating an initial population and calculating the fitness;
2.4, sequentially carrying out selection, crossing and variation operation on the population;
2.5, judging whether a convergence condition is met, if the convergence condition is not met, returning to the step 2.4, and if the convergence condition is met, executing the step 2.6;
2.6, decoding to obtain an optimal solution, namely an optimized MTSP model;
s3, obtaining the optimal structural unit number and the optimal topological structure according to the optimized MTSP model;
s3 specifically includes:
3.1, setting a plurality of groups of structural units;
3.2, sequentially substituting into S2, and obtaining corresponding optimal power values under different structural unit numbers by using a genetic algorithm;
3.3, carrying out cubic spline interpolation on the optimal power value obtained in the step 3.2 to obtain optimal peak power, wherein the number of the structural units corresponding to the optimal peak power and the combination of the thermoelectric modules in each structural unit are the optimal structural unit number and the optimal topological structure;
s4, constructing an automobile exhaust thermoelectric conversion device according to the optimal topological structure;
in 2.2, the number of parallel branches of the structural unit is set
N is the total number of the thermoelectric modules, when N is 1, segmentation is not needed, and S is 0; let the ith branch split occur after the Ti-th thermoelectric module, i is not less than 1<s-1, a better segmentation makes the imbalance factor Q as small as possible, where Q is defined as:
in the formula of UiVoltage of the Ti-th thermoelectric module, RiIs the internal resistance of the Ti-th thermoelectric module,
when Q is smaller, the corresponding peak power is larger; optimum segmentation satisfaction
2. The method for optimizing an electrical topology of an automotive exhaust thermoelectric conversion device according to claim 1, characterized in that: the S1 specifically includes:
1.1, maintaining constant cold source temperature and installation pressure;
1.2, measuring a group of voltage-current-power characteristic curves of a single thermoelectric module under different output current conditions;
1.3, changing the temperature of a heat source, and repeating 1.2;
and 1.4, according to the obtained voltage-current-power characteristic curve of the single thermoelectric module at different temperatures, equating the single thermoelectric module to a variable voltage source related to temperature difference and connecting a constant internal resistance in series, and establishing an equivalent circuit model.
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