An Optimization of New Energy Hybrid Configuration Parameters Based on GA Method
Configuration parameters of vehicular hybrid power systems (HPSs) are critical to their economy, weight, and fuel consumption. Many marine vehicles have parameters often set based on engineering experience when designing them, which often leads to excess power from power sources, increased costs, an...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/10/11/1747 |
| _version_ | 1797464935048937472 |
|---|---|
| author | Yifei Zhang Lijun Diao Chunmei Xu Jia Zhang Qiya Wu Haoying Pei Liying Huang Xuefei Li Yuwen Qi |
| author_facet | Yifei Zhang Lijun Diao Chunmei Xu Jia Zhang Qiya Wu Haoying Pei Liying Huang Xuefei Li Yuwen Qi |
| author_sort | Yifei Zhang |
| collection | DOAJ |
| description | Configuration parameters of vehicular hybrid power systems (HPSs) are critical to their economy, weight, and fuel consumption. Many marine vehicles have parameters often set based on engineering experience when designing them, which often leads to excess power from power sources, increased costs, and increased emissions. In this paper, a multi-objective optimization model, which includes the economic cost, weight, and fuel consumption, is proposed to evaluate the performance of configuration parameters. To optimize the objective optimization model, this paper adopts a genetic algorithm (GA) method to iteratively calculate the globally optimal configuration parameter results. Finally, three sets of different weight coefficients are used to verify the configuration optimization results when considering different optimization objectives. To verify the advantage of the multi-objective optimization method, the three sets of optimized results are compared to a specific configuration parameter of a marine vehicle. From the simulation results, compared with the original configuration scheme, the total economic cost of Scheme 1 is reduced by 37.25 × 10<sup>4</sup> $, the total weight is reduced by 213.55 kg, and the total fuel consumption is reduced by 163.64 t; the total economic cost of Scheme 2 is reduced by 12.2 × 10<sup>4</sup> $, the total weight is increased by 393.36 kg, and the total fuel consumption is reduced by 271.89 t; the total economic cost of Scheme 3 is reduced by 36.89 × 10<sup>4</sup> $, the total weight is reduced by 209.2 kg, and the total fuel consumption is reduced by 162.35 t. |
| first_indexed | 2024-03-09T18:14:17Z |
| format | Article |
| id | doaj.art-ef4ac0246b7a4b04b2675f627a070aa1 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T18:14:17Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-ef4ac0246b7a4b04b2675f627a070aa12023-11-24T08:52:17ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-11-011011174710.3390/jmse10111747An Optimization of New Energy Hybrid Configuration Parameters Based on GA MethodYifei Zhang0Lijun Diao1Chunmei Xu2Jia Zhang3Qiya Wu4Haoying Pei5Liying Huang6Xuefei Li7Yuwen Qi8School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaCRRC Changchun Railway Vehicles Co., Ltd., Changchun 130062, ChinaCRRC Changchun Railway Vehicles Co., Ltd., Changchun 130062, ChinaConfiguration parameters of vehicular hybrid power systems (HPSs) are critical to their economy, weight, and fuel consumption. Many marine vehicles have parameters often set based on engineering experience when designing them, which often leads to excess power from power sources, increased costs, and increased emissions. In this paper, a multi-objective optimization model, which includes the economic cost, weight, and fuel consumption, is proposed to evaluate the performance of configuration parameters. To optimize the objective optimization model, this paper adopts a genetic algorithm (GA) method to iteratively calculate the globally optimal configuration parameter results. Finally, three sets of different weight coefficients are used to verify the configuration optimization results when considering different optimization objectives. To verify the advantage of the multi-objective optimization method, the three sets of optimized results are compared to a specific configuration parameter of a marine vehicle. From the simulation results, compared with the original configuration scheme, the total economic cost of Scheme 1 is reduced by 37.25 × 10<sup>4</sup> $, the total weight is reduced by 213.55 kg, and the total fuel consumption is reduced by 163.64 t; the total economic cost of Scheme 2 is reduced by 12.2 × 10<sup>4</sup> $, the total weight is increased by 393.36 kg, and the total fuel consumption is reduced by 271.89 t; the total economic cost of Scheme 3 is reduced by 36.89 × 10<sup>4</sup> $, the total weight is reduced by 209.2 kg, and the total fuel consumption is reduced by 162.35 t.https://www.mdpi.com/2077-1312/10/11/1747configuration parametershybrid power systemmulti-objective optimization modelgenetic algorithm |
| spellingShingle | Yifei Zhang Lijun Diao Chunmei Xu Jia Zhang Qiya Wu Haoying Pei Liying Huang Xuefei Li Yuwen Qi An Optimization of New Energy Hybrid Configuration Parameters Based on GA Method Journal of Marine Science and Engineering configuration parameters hybrid power system multi-objective optimization model genetic algorithm |
| title | An Optimization of New Energy Hybrid Configuration Parameters Based on GA Method |
| title_full | An Optimization of New Energy Hybrid Configuration Parameters Based on GA Method |
| title_fullStr | An Optimization of New Energy Hybrid Configuration Parameters Based on GA Method |
| title_full_unstemmed | An Optimization of New Energy Hybrid Configuration Parameters Based on GA Method |
| title_short | An Optimization of New Energy Hybrid Configuration Parameters Based on GA Method |
| title_sort | optimization of new energy hybrid configuration parameters based on ga method |
| topic | configuration parameters hybrid power system multi-objective optimization model genetic algorithm |
| url | https://www.mdpi.com/2077-1312/10/11/1747 |
| work_keys_str_mv | AT yifeizhang anoptimizationofnewenergyhybridconfigurationparametersbasedongamethod AT lijundiao anoptimizationofnewenergyhybridconfigurationparametersbasedongamethod AT chunmeixu anoptimizationofnewenergyhybridconfigurationparametersbasedongamethod AT jiazhang anoptimizationofnewenergyhybridconfigurationparametersbasedongamethod AT qiyawu anoptimizationofnewenergyhybridconfigurationparametersbasedongamethod AT haoyingpei anoptimizationofnewenergyhybridconfigurationparametersbasedongamethod AT liyinghuang anoptimizationofnewenergyhybridconfigurationparametersbasedongamethod AT xuefeili anoptimizationofnewenergyhybridconfigurationparametersbasedongamethod AT yuwenqi anoptimizationofnewenergyhybridconfigurationparametersbasedongamethod AT yifeizhang optimizationofnewenergyhybridconfigurationparametersbasedongamethod AT lijundiao optimizationofnewenergyhybridconfigurationparametersbasedongamethod AT chunmeixu optimizationofnewenergyhybridconfigurationparametersbasedongamethod AT jiazhang optimizationofnewenergyhybridconfigurationparametersbasedongamethod AT qiyawu optimizationofnewenergyhybridconfigurationparametersbasedongamethod AT haoyingpei optimizationofnewenergyhybridconfigurationparametersbasedongamethod AT liyinghuang optimizationofnewenergyhybridconfigurationparametersbasedongamethod AT xuefeili optimizationofnewenergyhybridconfigurationparametersbasedongamethod AT yuwenqi optimizationofnewenergyhybridconfigurationparametersbasedongamethod |