Fast dynamic-programming algorithm for solving global optimization problems of hybrid electric vehicles
Owing to the comprehensive effects of dimensional disaster, interpolation error, and Markov characteristics of the controlled objects, the traditional dynamic programming algorithm has difficulty ensuring the efficiency of calculation, the accuracy of the results, and the rationality of the optimal...
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| Vydáno v: | Energy (Oxford) Ročník 273; s. 127207 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
15.06.2023
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| ISSN: | 0360-5442 |
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| Abstract | Owing to the comprehensive effects of dimensional disaster, interpolation error, and Markov characteristics of the controlled objects, the traditional dynamic programming algorithm has difficulty ensuring the efficiency of calculation, the accuracy of the results, and the rationality of the optimal control law when solving the optimal fuel economy problem for a multi-mode and multi-gear hybrid electric vehicles (HEVs). To solve this problem, an improved dynamic programming algorithm, CQU-DP, is proposed herein. The algorithm can rapidly generate optimal solutions with high accuracy and rationality through grid size configuration, matrix expansion, filtering, and the introduction of state and control variable penalties. The optimal fuel economy of a parallel HEV with five gears and six modes was solved using this algorithm. The results indicated that compared with the traditional basic dynamic programming algorithm (B-DP) and an improved dynamic programming algorithm (SJTU-DP), the proposed optimization algorithm reduced the calculation time by 96.36% and 93.79%, and the fuel economy was increased by 26.63% and 1.92%, respectively. Additionally, the optimal control law was more reasonable.
•Global optimal economy solution of hybrid electric vehicles (HEVs).•Account efficiency, accuracy, and rationality.•A fast dynamic-programming algorithm is developed.•The algorithm is verified by comparing the results with two other dynamic programming algorithms. |
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| AbstractList | Owing to the comprehensive effects of dimensional disaster, interpolation error, and Markov characteristics of the controlled objects, the traditional dynamic programming algorithm has difficulty ensuring the efficiency of calculation, the accuracy of the results, and the rationality of the optimal control law when solving the optimal fuel economy problem for a multi-mode and multi-gear hybrid electric vehicles (HEVs). To solve this problem, an improved dynamic programming algorithm, CQU-DP, is proposed herein. The algorithm can rapidly generate optimal solutions with high accuracy and rationality through grid size configuration, matrix expansion, filtering, and the introduction of state and control variable penalties. The optimal fuel economy of a parallel HEV with five gears and six modes was solved using this algorithm. The results indicated that compared with the traditional basic dynamic programming algorithm (B-DP) and an improved dynamic programming algorithm (SJTU-DP), the proposed optimization algorithm reduced the calculation time by 96.36% and 93.79%, and the fuel economy was increased by 26.63% and 1.92%, respectively. Additionally, the optimal control law was more reasonable.
•Global optimal economy solution of hybrid electric vehicles (HEVs).•Account efficiency, accuracy, and rationality.•A fast dynamic-programming algorithm is developed.•The algorithm is verified by comparing the results with two other dynamic programming algorithms. Owing to the comprehensive effects of dimensional disaster, interpolation error, and Markov characteristics of the controlled objects, the traditional dynamic programming algorithm has difficulty ensuring the efficiency of calculation, the accuracy of the results, and the rationality of the optimal control law when solving the optimal fuel economy problem for a multi-mode and multi-gear hybrid electric vehicles (HEVs). To solve this problem, an improved dynamic programming algorithm, CQU-DP, is proposed herein. The algorithm can rapidly generate optimal solutions with high accuracy and rationality through grid size configuration, matrix expansion, filtering, and the introduction of state and control variable penalties. The optimal fuel economy of a parallel HEV with five gears and six modes was solved using this algorithm. The results indicated that compared with the traditional basic dynamic programming algorithm (B-DP) and an improved dynamic programming algorithm (SJTU-DP), the proposed optimization algorithm reduced the calculation time by 96.36% and 93.79%, and the fuel economy was increased by 26.63% and 1.92%, respectively. Additionally, the optimal control law was more reasonable. |
| ArticleNumber | 127207 |
| Author | Hu, Minghui Guo, Shanqi Chen, Shuang |
| Author_xml | – sequence: 1 givenname: Shuang surname: Chen fullname: Chen, Shuang organization: State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China – sequence: 2 givenname: Minghui orcidid: 0000-0001-7551-5883 surname: Hu fullname: Hu, Minghui email: minghui_h@163.com organization: State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China – sequence: 3 givenname: Shanqi surname: Guo fullname: Guo, Shanqi organization: State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China |
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| Keywords | Accuracy of result Efficiency of calculation Dynamic programming Hybrid electric vehicles Rationality of result |
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| SubjectTerms | Accuracy of result algorithms Dynamic programming Efficiency of calculation energy fuels Hybrid electric vehicles Rationality of result |
| Title | Fast dynamic-programming algorithm for solving global optimization problems of hybrid electric vehicles |
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