The performance of power batteries directly impacts the range, safety, and reliability of pure electric ships. Currently, mainstream power batteries primarily consist of lithium-ion batteries, including lithium iron phosphate and lithium ternary variants. However, temperature significantly affects the performance of lithium batteries. In low-temperature environments, lithium-ion battery cycle life deteriorates significantly, battery power characteristics rapidly decline, usable capacity diminishes notably, and challenges such as low temperature charging and lithium precipitation arise. At high temperatures, lithium battery self-discharge rates increase, capacity loss is pronounced, battery aging accelerates, and issues like overheating leading to thermal runaway, combustion, and explosion emerge. Hence, thermal management of lithium batteries is a critical area requiring breakthroughs in current technology. This paper reviews research progress in lithium battery thermal management system (BTMS) globally, detailing methods and advancements in low-temperature and high-temperature thermal management, and outlines future development trends in lithium BTMS.
To address the problems of overfitting judgment results and the impact of the parameter on the accuracy of intelligent algorithms in fault diagnosis of the dredger pump, based on the analysis of common faults of centrifugal pumps, in this paper, a fault diagnosis method combining Support Vector Machine (SVM) and Bacterial Foraging Algorithm(BFA) for dredger pumps was proposed. By using the optimization ability of BFA, the optimal SVM penalty factor and kernel parameters are found, which improves the fault diagnosis ability. The superiority of the proposed method is verified by comparative analysis through simulation and actual examples. The results show that BFA has better optimization ability than traditional SVM.
In order to improve the dynamic positioning control performance of the trailing suction dredger. In this paper, an improved particle swarm optimization PID control method is proposed for the dredging process of offshore operations. And the upper computer monitoring technology has been introduced to visually display the status of the suction dredger during offshore dredging. This paper designs the dynamic positioning controller based on the improved particle swarm optimization PID algorithm, and uses Labview to design the monitoring interface of the upper computer, which intuitively shows the reliability of Dynamic positioning.
During the actual construction of a cutter suction dredger, the control of mud density and flow rate in the mud pipeline are two important parameters that affect the dredging output and the safe transportation of mud. Mud density is affected by many factors such as the speed of the cutter, the speed of the cutter and the speed of the submerged pump. Real-time prediction and control of mud density are important means to achieve efficient and safe dredging. Using actual construction data, an optimization method based on Sparrow Search Algorithm (SSA) is proposed to predict mud density. LSTM and ELMAN models are established and compared with the optimized SSA-LSTM and SSA-ELMAN models. The results show that the SSA optimization method can predict the density of cutter suction dredger with high prediction accuracy and stability.
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