Lithium titanate batteries are widely used as an energy source for high energy laser systems. In order to improve the reliability of high energy laser systems, this study established a life prediction model for lithium titanate batteries based on the classical one-dimensional Wiener process, adopted the Bayesian method for remaining useful life (RUL) prediction, and updated model parameters with typical working condition data from similar products to improve prediction accuracy, narrowing the prediction confidence interval to within 10 cycles. Experimental results show that the proposed method has high accuracy and feasibility.
Power battery is the energy source of high power laser system. The starting voltage is the key index to judge the working state of power battery. In order to estimate the power battery voltage and optimize the power supply control effect of high power laser system, based on the measured data of power cell, the least square method was used to fit the internal resistance curve, and the expression of the starting voltage of the power battery was deduced, which was applied to the evaluation test of the energy storage battery in the high power laser system. The application results show that the accuracy of starting voltage estimation of a certain type of lithium titanate power battery can reach more than 95%, which effectively meets the demand of system power supply control. The function fitting and the method of power battery starting voltage derivation developed in this paper are universal and provide a reference for the starting voltage estimation of other types of battery cells and other related application conditions.
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