An Energy Management Strategy for Hybrid Ship Based on SVM and MPC

To improve the energy efficiency and fuel economy of hybrid ships, energy management strategy for hybrid ships based on support vector machine (SVM) and model predictive control (MPC) is proposed, incorporating the working condition recognition into the strategy management. A working-condition recognition model is developed based on the SVM theory, the kernel function type and key parameters are optimized by using the operation data from the hybrid powered cruise ships called"MEIWEI KEYUE", and the one of four working conditions is recognized by feeding the real-time operation data. An energy management MPC model (EM-MPC) is proposed, allocating the output powers of main engine and composite ESUs for minimizing the consumption of fuel and maintaining the state of charge (SOC) stabilization of energy storage unit (ESU), which is constrained by the power demand prediction (PDP) model. Then, a prediction model based on the multi-step Markov model is proposed to improve the accuracy of PDP under different working conditions, and the PDP constraint in the EM-MPC model is updated based on the recognized working condition, which contributes to the real-time power allocation. The optimal solution is decomposed into high frequency signal and low frequency signal by wavelet transform method, and these signals are assigned to the super capacitor with high power density and the battery with high energy density, respectively. To validate the proposed strategy, a simulation via Matlab is introduced and the results show that: ① the cumulative fuel consumption of the proposed method is 4 404.556 1 g and the average fuel consumption rate is 202.9737 g/kWh; ② under the same working condition, the fuel consumption can be saved by 4.55% via the proposed EM-MPC, comparing with the traditional method.