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Simultaneous optimization of topology, control and size for multi-mode hybrid tracked vehicles

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  • Qin, Zhaobo
  • Luo, Yugong
  • Zhuang, Weichao
  • Pan, Ziheng
  • Li, Keqiang
  • Peng, Huei

Abstract

Hybrid tracked vehicles have become increasingly popular for off-road applications due to their better fuel economy and higher output power. Currently, the most popular in the production of tracked vehicles are the series hybrid, because of the simple powertrain designs. However, they suffer from high energy conversion losses and large propulsion motors. To overcome these issues, multi-mode hybrid tracked vehicles are employed since they have high efficiency and excellent overall performance. The proposed multi-mode hybrid powertrain can realize straight driving, turning, and driving backwards without any additional steering mechanism. To systematically explore all the possible designs of multi-mode hybrid designs with planetary gears, a topology-control-size-integrated optimization approach is presented. A novel near-optimal energy management strategy, Efficiency Evaluation Real-time Control Strategy (EERCS), is proposed to rapidly calculate near-optimal control rules for design candidates. The EERCS is confirmed to achieve results similar to those of Dynamic Programming (DP), yet the computation time is over 50 times less. With the help of EERCS, the optimal design together with its parameters is computed using multi-objective optimization based on a meta-heuristic algorithm. Results of a case study show that the optimized design with downsized components produces improved drivability and fuel economy compared to the series hybrid benchmark.

Suggested Citation

  • Qin, Zhaobo & Luo, Yugong & Zhuang, Weichao & Pan, Ziheng & Li, Keqiang & Peng, Huei, 2018. "Simultaneous optimization of topology, control and size for multi-mode hybrid tracked vehicles," Applied Energy, Elsevier, vol. 212(C), pages 1627-1641.
    • Handle: RePEc:eee:appene:v:212:y:2018:i:c:p:1627-1641
    DOI: 10.1016/j.apenergy.2017.12.081
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    References listed on IDEAS

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    4. Yang, Yalian & Li, Pengshuai & Pei, Huanxin & Zou, Yunge, 2022. "Design of all-wheel-drive power-split hybrid configuration schemes based on hierarchical topology graph theory," Energy, Elsevier, vol. 242(C).
    5. Huijun Yue & Jinyu Lin & Peng Dong & Zhinan Chen & Xiangyang Xu, 2023. "Configurations and Control Strategies of Hybrid Powertrain Systems," Energies, MDPI, vol. 16(2), pages 1-18, January.
    6. Lihua Wang & Yahui Cui & Fengqi Zhang & Guanglei Li, 2020. "Architectures of Planetary Hybrid Powertrain System: Review, Classification and Comparison," Energies, MDPI, vol. 13(2), pages 1-24, January.
    7. Chen, Shuang & Hu, Minghui & Lei, Yanlei & Kong, Linghao, 2023. "Novel hybrid power system and energy management strategy for locomotives," Applied Energy, Elsevier, vol. 348(C).
    8. García, Antonio & Carlucci, Paolo & Monsalve-Serrano, Javier & Valletta, Andrea & Martínez-Boggio, Santiago, 2020. "Energy management strategies comparison for a parallel full hybrid electric vehicle using Reactivity Controlled Compression Ignition combustion," Applied Energy, Elsevier, vol. 272(C).
    9. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    10. Zhou, Xingyu & Sun, Chao & Sun, Fengchun & Zhang, Chuntao, 2023. "Commuting-pattern-oriented stochastic optimization of electric powertrains for revealing contributions of topology modifications to the powertrain energy efficiency," Applied Energy, Elsevier, vol. 344(C).
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