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A Hybrid System Approach to Energy Optimization in Gas–Electric Hybrid Powertrains

Author

Listed:
  • Xiaojun Sun

    (School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121000, China)

  • Benrong Zhang

    (School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121000, China)

  • Jiangning Zhu

    (Department of Mathematics and Computer Science, Chaoyang Normal University, Chaoyang 122000, China)

  • Chong Yao

    (Yantai Research Institute, Harbin Engineering University, Harbin 150001, China)

Abstract

Amid growing global concerns over environmental sustainability, the shipping industry is under increasing pressure to implement innovative power systems that minimize ecological impact. A promising approach is the marine gas–electric hybrid system, which combines conventional marine propulsion with electric power to offer a cleaner energy solution. Characterized by the integration of continuous and discrete variables, these systems reflect the hybrid nature of gas–electric propulsion. Despite their potential, research on marine hybridization remains limited. To address this gap, a hybrid system model has been developed to optimize energy allocation while accurately capturing the hybrid characteristics of gas–electric systems in ships. Additionally, an energy distribution strategy based on predictive control has been proposed to validate the model’s practical applicability. A weighted evaluation method was employed on a marine gas–electric hybrid test platform to verify the performance of both the model and the control strategy. Results show that different weighting configurations lead to varying torque distribution patterns, confirming the effectiveness of the hybrid system model. Moreover, tuning the weighting parameters within the energy allocation strategy yields diverse control behaviors, further demonstrating the system’s viability for marine applications.

Suggested Citation

  • Xiaojun Sun & Benrong Zhang & Jiangning Zhu & Chong Yao, 2025. "A Hybrid System Approach to Energy Optimization in Gas–Electric Hybrid Powertrains," Sustainability, MDPI, vol. 17(18), pages 1-27, September.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8160-:d:1746600
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    References listed on IDEAS

    as
    1. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Liu, Zhijiang & Ke, Yun & Ding, Shunliang, 2023. "Novel enhancement of energy distribution for marine hybrid propulsion systems by an advanced variable weight decision model predictive control," Energy, Elsevier, vol. 274(C).
    2. Mansoor, Muhammad & Stadler, Michael & Zellinger, Michael & Lichtenegger, Klaus & Auer, Hans & Cosic, Armin, 2021. "Optimal planning of thermal energy systems in a microgrid with seasonal storage and piecewise affine cost functions," Energy, Elsevier, vol. 215(PA).
    3. Haibo Zhao & Yahong Xing & Chengpeng Zhou & Yao Wang & Hui Duan & Kai Liu & Shigong Jiang, 2024. "Research on Hybrid Logic Dynamic Model and Voltage Predictive Control of Photovoltaic Storage System," Energies, MDPI, vol. 17(17), pages 1-22, August.
    4. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Yang, Qidong & Yang, Xuchang, 2022. "Optimal control of transient processes in marine hybrid propulsion systems: Modeling, optimization and performance enhancement," Applied Energy, Elsevier, vol. 321(C).
    5. Yan Zhang & Xiaoli Chu & Yang Liu & Yongqiang Liu, 2019. "A Modelling and Control Approach for a Type of Mixed Logical Dynamical System Using in Chilled Water System of Refrigeration System," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, March.
    6. Dassios, Ioannis & Vaca, Angel & Milano, Federico, 2024. "On hybrid dynamical systems of differential–difference equations," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
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