Optimizing energy management strategies for hybrid electric ships based on condition identification and model predictive control

ABSTRACT

To improve the fuel economy of ships with multi-energy hybrid power systems, we propose an energy management strategy for hybrid power ships based on condition identification and prediction. By constructing a condition identification model based on a support vector machine (SVM), the characteristic parameters of current operation conditions of the ships were analyzed and assessed to determine operation condition types of the ships in real-time. The model predictive control (MPC) method is used to distribute the output power of the main engine, storage battery, and supercapacitor. Using multiple sets of multi-step Markov models with different types of operating conditions as the power prediction model, the optimal prediction time domain length and initial SOC are selected through simulation. Finally, based on the operating condition identification results, the operating condition prediction model for the corresponding type of operating condition is automatically selected to improve the strategy performance.The results show that the proposed strategy can save 4.55% of fuel consumption compared with the strategy using only model predictive control.Using this strategy can effectively improve the fuel economy and energy efficiency, and promote the development of green ships.

KEYWORDS:

• Hybrid power systems
• energy management strategy
• support vector machine
• model predictive control
• fuel consumption
• operation conditions

Acknowledgements

This work is Supported by National Key R&D Program of China (No: 2021YFB2601601)

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

The work was supported by the National Key Research and Development Program of China [No: 2021YFB2601601].