Orthogonal Optimization Design of Hybrid Vehicles
Since the hybrid car is powered by both the engine and the electric motor, its design is much more complicated than that of a conventional car powered by a single power source (engine). The design of a traditional car generally starts from its dynamic design index, and the power of the engine is designed based on this. For hybrid vehicles, although the total power requirements can also be determined by the dynamics, the choice of the engine and the motor power in the end, as well as the selection of the battery capacity and the voltage level, are issues that the hybrid vehicle design needs to consider. How to optimize design is the core and important content of hybrid technology.
To determine an optimal solution, simulation design is generally used. In foreign countries, hybrid vehicles are mostly designed through this method of simulation. The literature applied nearly 1.8 million simulations. In total, 100 computers were used in parallel and it took nearly a week to complete. Then a lot of data analysis is performed on these simulation results, and the best one is selected. It can be seen that the traditional simulation design method has low computational efficiency, long design cycle and time-consuming and laborious, and it is difficult to apply directly to the design of hybrid vehicles. Therefore, it is particularly important to seek a more simple and intuitive design method. Orthogonal experimental design method is a simple and intuitive optimization design method, especially for the multi-program optimization problem. As the hybrid vehicles currently under study mainly examine the fuel economy, it is necessary to determine the factors affecting fuel economy before carrying out the orthogonal optimization design of hybrid vehicles.
The test factors of hybrid vehicles and the design of horizontal hybrid vehicles are how to determine the parameters of each powertrain under the premise of guaranteeing the power requirements, so as to optimize the fuel economy/discharge efficiency. Here, fuel economy is the design goal, so that it reaches the optimal value. The fuel economy of hybrid vehicles has many influencing factors. The main influencing factors are the degree of mixing H, the battery capacity C, the battery voltage level V, and the final gear ratio i 0. Therefore, these four parameters are selected for optimal design. Among them, the degree of mixing H is defined as the percentage of the total power P total of the electrical system power P elec, ie H = P elec P total × 100% P total = P elec + P eng where Peng g - engine power through dynamics The design index can determine the total power P total. Therefore, if the degree of mixing is determined, the two power sources can be calculated by the above formula. Therefore, the four influencing factors of the hybrid vehicle selected above basically include the main aspects of hybrid vehicle design.
The various conditions that the factor is in the test or the different values ​​taken are called the level of the factor. If a factor takes K states or K values, the factor is said to be K level factor.
The orthogonal design of the optimal design of the hybrid vehicle is based on the above-identified factors. In order to further reduce the number of simulation tests, the L 9 (22x32) mixed orthogonal table is selected. Of these, 9 is the total number of trials. If the total number of trials is 36, it can significantly reduce the number of simulation trials. The 2 2 3 2 represents four factors, the first two factors are two levels, and the latter two factors are three levels of mixed orthogonal experiments.
The two mixing degrees were examined under a total power source of 160 kW: H 1 was 140 kW for the engine, the motor was 20 kW, and the degree of mixing was 12.5%; H 2 was the engine power 100 kW, and the motor power was 60 kW, the level of mixing is 37.5%.
Battery voltage level V is determined as two values ​​according to actual needs, ie V 1 is a 300 V electrical system and V 2 is a 336 V electrical system.
Battery capacity C selects three different capacities of the same type (NIH). C 1 is 30 A. h, C 2 is 60 A? h, C 3 is 90 A? h.
The speed ratio of the main reducer i 0 must be selected within the range that satisfies the dynamic requirements. That is, the maximum speed ratio of the final reducer must meet the maximum grade requirement and the minimum value must meet the maximum vehicle speed requirement. It is determined that i 01 is 5.0, and i 02 is 5. 7, i 03 is 6.33.
1 is a mixed orthogonal test table designed.
1 Four-factor mixed orthogonal test The mixed orthogonal table designed in Table 1 also satisfies the basic properties of the orthogonal table, namely, orthogonality, balanced dispersivity, and comprehensive comparability. Therefore, the subsequent simulation design results can be compared directly from the table and the optimal set of solutions can be selected.
The simulation and optimization design deals with the four influencing factors of the fuel economy yi of the hybrid vehicle proposed above. The appropriate orthogonal table L 9 (2 2 × 3 2) is used to prepare the simulation test program as shown. The simulation software ADVISOR is used for simulation, and the simulation result is directly filled in the column of the test index, ie the last column. The simulation result is a unified SOC-checked value. Therefore, the index value is comparable.
According to the index value of the above simulation test, it can be processed by a range analysis method (also referred to simply as R method). The calculation contents and main steps of this range analysis method are as shown below. Among them, y jk is the experimental index corresponding to the jth factor k level, and y jk is the average of yjk. The optimal level of j factor can be judged from the yjk size, and the combination of the optimal levels of each factor is the optimal design combination. In addition, R j is the extreme difference of the jth factor, and its calculation formula is R j = max(y j1, y j2,...)- min(y j1, y j2,...) R j reflects the change of the jth factor level. The extent of change in the test index. The larger R j , the greater the influence of this factor on the indicator, the more important it is. According to the size of the range R j , the primary and secondary factors can be determined. This range analysis method fully reflects the flexibility and intuitiveness of orthogonal design, so this processing method is also called visual analysis.
According to the above processing method, fill in the calculation results into the lower part. The results show that the primary and secondary factors affecting the index value are the degree of mixing H, the battery capacity C, the main reduction ratio i 0 and the voltage level V, and the level of excellence of each factor can also be seen intuitively. Finally, the best set of options can be singled out, as shown in the last line. The simulation test according to this scheme found that its fuel economy index was 46.2 L/(100 km). It can be seen from the above that this is the optimal solution, and the index value calculated by the orthogonal experimental scheme configured according to China is smaller.
End
The orthogonal optimization design theory is applied to the optimal design of hybrid vehicles. The orthogonality, equalization and intuition of orthogonal tables are used to study the optimization of multi-scheme of hybrid vehicles. Not only can the number of simulation tests be greatly reduced, but also Can find out the optimal design scheme among them effectively and visually.
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