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Design space exploration for waste heat recovery system in automotive application under driving cycle

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  • Zhao, Mingru
  • Canova, Marcello
  • Tian, Hua
  • Shu, Gequn

Abstract

Organic Rankine Cycle (ORC) systems have been recently considered for the promising application to Waste Heat Recovery (WHR) in automotive application. However, the design of ORC systems for highly transient operations typical of heavy-duty vehicles faces several challenges. This work extends the method of Design Space Exploration (DSE) to optimize the design of an ORC system for heavy-duty vehicle applications. The goal is to match the size of ORC system with the feature areas of driving cycle. Starting from drive cycle data, the feature areas are identified to represent the driving cycle. Then, a Weighted Least Square method is adopted to determine the initial design condition from feature areas, and based on the design condition, the initial ORC model is built for the further optimization. Particle Swarm Optimization (PSO) with parallel computation is adopted to optimize the performance of nonlinear ORC system with coupled constraints. The result shows that the optimal system can output 6.87% more power than the initial system and 20.08% more power than the inferior system over the feature areas of WHTC test. The analysis shown in this work also includes general design guidelines for optimizing the system architecture and optimally sizing the heat exchangers to improve the ORC thermal efficiency.

Suggested Citation

  • Zhao, Mingru & Canova, Marcello & Tian, Hua & Shu, Gequn, 2019. "Design space exploration for waste heat recovery system in automotive application under driving cycle," Energy, Elsevier, vol. 176(C), pages 980-990.
    • Handle: RePEc:eee:energy:v:176:y:2019:i:c:p:980-990
    DOI: 10.1016/j.energy.2019.04.063
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    References listed on IDEAS

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    Cited by:

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    6. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Wang, Yan & Lei, Biao & Wu, Yuting, 2022. "Performance limits of the single screw expander in organic Rankine cycle with ensemble learning and hyperdimensional evolutionary many-objective optimization algorithm intervention," Energy, Elsevier, vol. 245(C).
    7. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Yao, Baofeng & Wang, Yan, 2022. "An outlier removal and feature dimensionality reduction framework with unsupervised learning and information theory intervention for organic Rankine cycle (ORC)," Energy, Elsevier, vol. 254(PB).

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