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

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  • Chen, Shuang
  • Hu, Minghui
  • Guo, Shanqi

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.

Suggested Citation

  • Chen, Shuang & Hu, Minghui & Guo, Shanqi, 2023. "Fast dynamic-programming algorithm for solving global optimization problems of hybrid electric vehicles," Energy, Elsevier, vol. 273(C).
    • Handle: RePEc:eee:energy:v:273:y:2023:i:c:s0360544223006011
    DOI: 10.1016/j.energy.2023.127207
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    References listed on IDEAS

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    2. Haochen Xu & Niaona Zhang & Zonghao Li & Zichang Zhuo & Ye Zhang & Yilei Zhang & Haitao Ding, 2023. "Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios," Sustainability, MDPI, vol. 15(22), pages 1-16, November.

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