Fast dynamic-programming algorithm for solving global optimization problems of hybrid electric vehicles

被引：29

作者：

Chen, Shuang ^{[1
,2
]}

Hu, Minghui ^{[1
,2
]}

Guo, Shanqi ^{[1
,2
]}

机构：

[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China

[2] Chongqing Univ, Sch Automot Engn, Chongqing 400044, Peoples R China

来源：

ENERGY | 2023年 / 273卷

关键词：

Dynamic programming; Hybrid electric vehicles; Efficiency of calculation; Accuracy of result; Rationality of result; DESIGN OPTIMIZATION; STRATEGY;

D O I：

10.1016/j.energy.2023.127207

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

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.

引用

页数：18

共 31 条

[1] Fuel cell electrified propulsion systems for long-haul heavy-duty trucks: present and future cost-oriented sizing [J].