Energy management is a critical and challenging factor required for efficient and safe operation of underwater gliders (UGs), and the energy consumption model (ECM) is indispensable. In this paper, a more complete ECM of UGs is established, which considers ocean currents, seawater density variation, deformation of the pressure hull, and asymmetry of gliding motion during descending and ascending. Sea trial data are used to make a comparison between ECMs with and without the consideration of ocean currents, and the results prove that the ECM that considers the currents has a significantly higher accuracy. Then, the relationship between energy consumption and multiple parameters, including gliding velocity relative to the current, absolute gliding angle, and diving depth, is revealed. Finally, a simple example is considered to illustrate the effects of the depth-averaged current on the energy consumption.

Combination prediction models have gained great development in the area of information science, and are widely applied in engineering fields. The underwater glider (UG) is a new type of unmanned vehicle used in ocean observation for the advantages of long endurance, low noise, etc. However, due to its lower speed relative to the ocean current, the surfacing positioning point (SPP) of an UG often drifts greatly away from the preset waypoint. Therefore, this paper proposes a new combination model for predicting the SPP at different time scales. First, the kinematic model and working flow of the Petrel-L glider is analyzed. Then, this paper introduces the principles of a newly proposed combination model which integrates single prediction models with optimal weight. Afterwards, to make an accurate prediction, ocean current data are interpolated and averaged according to the diving depth of UGs as an external influencing factor. Meanwhile, with sea trial data collected in the northern South China Sea by Petrel-L, which had a total range of 4230.5 km, SPPs are predicted using single prediction models at different time scales, and the combination weights are derived with a novel simulated annealing optimized Frank–Wolfe method. Finally, the evaluated results demonstrate that the MAE and MSE are 966 m and 969 m, which proves that the single models achieved good performance under specified situations, and the combination model performed better at full scale because it integrates the advantages of the single models. Furthermore, the predicted SPPs will be helpful in the dead reckoning of the UG, and the proposed new combination method could extend into other fields for prediction.

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