The on-board energy storage system plays a key role in electric vehicles since it directly affects their performance and autonomy. The lithium-ion battery offers satisfactory characteristics that make electric vehicles competitive with conventional ones. This article focuses on modeling and estimating the parameters of the lithium-ion battery cell when used in different electric vehicle drive cycles and styles. The model consists of an equivalent electrical circuit based on a second-order Thevenin model. To identify the parameters of the model, two algorithms were tested: Trust-Region-Reflective and Levenberg-Marquardt. To account for the dynamic behavior of the battery cell in an electric vehicle, this identification is based on measurement data that represents the actual use of the battery in different conditions and driving styles. Finally, the model is validated by comparing simulation results to measurements using the mean square error (MSE) as model performance criteria for the driving cycles (UDDS, LA-92, US06, neural network (NN), and HWFET). The results demonstrate interesting performance mostly for the driving cycles (UDDS and LA-92). This confirms that the model developed is the best solution to be integrated in a battery management system of an electric vehicle.

In radiomobile contexte, radio frequency spectrum is a ressource that needs to be used with appropriate efficiency. This can be achieved by the mean of spectrum sensing operation. This function consists to analyze the occupancy of the radio frequency spectrum in order to detect which bands are unused. This concept is largely appreciated in cognitive radio where more flexibility is required to adapt to the communication environment. Different techniques are presented in the literature. In this paper, we are interested by the application of the energy detector method for spectrum sensing. This application is performed in cognitive radio systems with the use of random sampling. The performance of this approach is evaluated in term of its receiver operating characteristic curve and compared to the uniform sampling case.

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International audienceIn this paper, we are interested to the implementation of the spectrum sensing (SS) operation based on a real signal of the FM radio and using a random sampling mode. This application consists on the realization of the spectrum sensing operation based on the energy detector (ED) method combined with random sampling. The analyzed real signal is an FM radio signal captured under GNU-Radio. This application is performed in cognitive radio (CR) systems in order to analyze the occupancy of the radio frequency spectrum. The performance of this approach is evaluated in terms of its receiver operating characteristic curves (ROC curves) and in terms of the false alarm probability. © 2016 IEEE

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.