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Lithium-ion batteries are the main power supply equipment in many fields due to their advantages of no memory, high energy density, long cycle life and no pollution to the environment. Accurate prediction for the remaining useful life (RUL) of lithium-ion batteries can avoid serious economic and safety problems such as spontaneous combustion. At present, most of the RUL prediction studies ignore the lithium-ion battery capacity recovery phenomenon caused by the rest time between the charge and discharge cycles. In this paper, a fusion method based on Wasserstein generative adversarial network (GAN) is proposed. This method achieves a more reliable and accurate RUL prediction of lithium-ion batteries by combining the artificial neural network (ANN) model which takes the rest time between battery charging cycles into account and the empirical degradation models which provide the correct degradation trend. The weight of each model is calculated by the discriminator in the Wasserstein GAN model. Four data sets of lithium-ion battery provided by the National Aeronautics and Space Administration (NASA) Ames Research Center are used to prove the feasibility and accuracy of the proposed method. 相似文献
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快速收敛的认知无线电功率控制算法 总被引:3,自引:0,他引:3
针对认知无线电系统中不同认知用户的通信需求,以码分多址(CDMA,code division multiple access)认知无线电系统为通信平台,通过设计一种有效的代价函数,提出了一种新的非合作功率控制博弈算法,并证明了该算法纳什均衡的存在性和唯一性.仿真结果表明,与其他几种功率控制算法相比较,该算法具有较快的收敛速度,能够以较低的功率水平满足不同用户的信干比要求. 相似文献
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