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Net buoyancy changes affect hovering performance and increase the extra energy consumption of deep–sea intelligent floats (DIFs) at different submergence depths. To improve the adjustment efficiency of net buoyancy changes and solve the extra energy consumption problem at different submergence depths, this study proposes a combination model of ballasting weight (BW) and minimum oil content for DIFs based on the BW method. The proposed model is accurately designed by combining an exponential function model of actual density distribution in the oceans and the deformation of a DIF hull that resulted from compressibility and thermal expansibility. Given that the adjustment process of the net buoyancy changes should be approximated for actual deployment, the model establishes the compressibility balanced relationship between the DIF hull and actual seawater. The feasibility of the proposed model is validated through hyperbaric tank system and at–sea experiments. Experimental results show that the relationship between the net buoyancy changes of DIF and pressure is nonlinear. The BW changes fluctuate between 0.05 and 0.15 g by increasing the diving depth by 1 m. For the influence of the BW on the submergence depth error, the scope of the depth error ratio is 2.2% to 2.8% from the predefined depth of 100–4200 m. Analysis results provide a basis for the establishment of an optimal net buoyancy adjustment scheme for DIFs.

Ballasting Weight on Net Buoyancy Changes and Submergence Depth for a Spherical Buoyancy–Driven Intelligent Float Based on the Ballasting Method