Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting




composite electric power system, ship, energy management, model predictive control, power prediction, neural network, variational modal decomposition


A proposed solution is presented to address the issue of rising energy loss resulting from inaccurate power prediction in the predictive energy management strategy for composite electric power electric ship. The solution involves the development of a power prediction model that integrates Archimedes' algorithm, optimized variational modal decomposition, and BiLSTM. Within the framework of Model Predictive Control, this predictive model is utilized for power forecasting, transforming the global optimization problem into one of optimizing the power output distribution among power sources within the predictive time domain, then the optimization objective is to minimize the energy loss of the composite electric power system, and a dynamic programming algorithm is employed to solve the optimization problem within the forecast time domain. The simulation findings demonstrate a significant enhancement in the forecast accuracy of the power prediction model introduced in this study, with a 52.61% improvement compared to the AOA-BiLSTM power prediction model. Concurrently, the energy management strategy utilizing the prediction model proposed in this research shows a 1.02% reduction in energy loss compared to the prediction model control strategy based on AOA-BiLSTM, and a 15.8% reduction in energy loss compared to the ruler-based strategy.


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How to Cite

Chen H, Huang X. Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting . EAI Endorsed Trans Energy Web [Internet]. 2024 Apr. 3 [cited 2024 Apr. 25];11. Available from:



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