Adaptive equivalent consumption minimization strategy for hybrid electric ship

In recent years, with the development of battery technology, hybrid electric ship (HES), as a promising solution to reduce fuel consumption and emissions, has become research hotspot. However, frequent use will accelerate aging battery, replacement scrapped increase cost ship. Therefore, it is necessary consider delaying into energy control strategy HES. The equivalent minimization (ECMS) feasible because can be implemented in real time. under condition uncertain initial state charge (SOC) ECMS cannot effectively unless factor (EF) optimized this paper, an adaptive (A-ECMS) proposed, which extracts global optimal EF trajectory from dynamic programming (DP) uses back propagation (BP) neural network adjust A trade-off between made function by introducing weight coefficient. Finally, effectiveness adaptability proposed are verified MATLAB.