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Modeling and adaptive control for supercapacitor in automotive applications based on artificial neural networks

机译:基于人工神经网络的汽车应用超级电容器建模与自适应控制

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The behavior of a supercapacitor is a complex and nonlinear function of its current rate, temperature, chemistry and history, and hence cannot easily be determined. In this study, we use a one-layer feedforward artificial neural network (ANN), trained using the back-propagation algorithm, to model the behavior of supercapacitors used in automotive applications. Possible improvements of the neural network model using a multilevel approach are discussed. Then, on the basis of this model, a neural controller is developed in order to control the supercapacitor voltage. Simulation results confirmed the accuracy of the model compared to measurements from supercapacitor module power-cycling.
机译:超级电容器的行为是其电流速率,温度,化学性质和历史的复杂且非线性的函数,因此无法轻松确定。在这项研究中,我们使用经过反向传播算法训练的单层前馈人工神经网络(ANN)对汽车应用中超级电容器的行为进行建模。讨论了使用多级方法对神经网络模型的可能改进。然后,在此模型的基础上,开发了神经控制器以控制超级电容器电压。与超级电容器模块电源循环的测量结果相比,仿真结果证实了模型的准确性。

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