Energy management strategy for a hybrid tug based on explicit model predictive control

Rongcheng Xu; Xu Zhao; Wei Yuan

doi:10.1117/12.3030331

5 June 2024 Energy management strategy for a hybrid tug based on explicit model predictive control

Rongcheng Xu, Xu Zhao, Wei Yuan

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131632A (2024) https://doi.org/10.1117/12.3030331
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

To enhance fuel efficiency and address the challenge of high computational demands associated with Model Predictive Control (MPC), which hinders real-time implementation in the energy management of hybrid propulsion tugs, this paper proposes an Energy Management Strategy (EMS) based on Explicit Model Predictive Control (EMPC). Initially, a parallel hybrid propulsion tug system model consisting of a diesel engine, electric motor, energy storage system, and gearbox is established. Subsequently, by formulating the energy management optimal control problem with fuel consumption and emissions as the objective function, the problem is transformed into a multi-parameter quadratic optimization problem. The EMPC control law is obtained by offline solving the multi-parameter quadratic optimization problem. Finally, simulation results under typical operating conditions demonstrate that the EMPC-based energy management strategy improves fuel efficiency by 18.9% and reduces the strategy implementation time by 90.9%, showcasing its significant potential for engineering applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rongcheng Xu, Xu Zhao, and Wei Yuan "Energy management strategy for a hybrid tug based on explicit model predictive control", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131632A (5 June 2024); https://doi.org/10.1117/12.3030331

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