基于参数辨识方法的轻量化无人水下航行器无线充电系统的恒流恒压充电策略

Wireless power transfer (WPT) technology is the most potential power replenishment for unmanned underwater vehicles (UUVs) due to its safety and automation. Furthermore, lightweight design of UUVs is a pressing pursuit. To this end, a DC input-output model of the LCC-S compensated WPT system is established, eliminating the need for additional power electronics and communication devices on UUVs. To improve output prediction accuracy and enable constant current-constant voltage (CC-CV) charging, the back propagation neural network (BPNN) prediction model and control strategy without secondary side feedback is first proposed, which uses the primary side DC information to predict the output of the UUV side with the help of BPNN, simplifies data acquisition and processing, enabling UUV lightweighting design and CC-CV wireless charging. The BPNN’s nonlinear fitting capability addresses system components that are difficult to model accurately, such as Zero Voltage Switching (ZVS), conductor and eddy current losses, thus improving output prediction accuracy. Simulations and experiments show that the system performs well in startup, steady-state accuracy and transient response. An experimental prototype was constructed, and the proposed BPNN method achieved a maximum steady-state prediction error of 2.21% and 1.369%, compared to the measured current and voltage. The adjustment time is approximately 1 second during the start-up and sudden load changes.