<p>Autonomous and grid-connected systems play an important role in the<br />massive integration of renewable energy sources necessary for the global<br />development of a sustainable society. In this regard, the analysis of the<br />behavior of electrochemical storage devices such as lead-acid batteries<br />installed on hybrid energy systems and microgrids in terms of lifespan and economic profitability is an important research subject. The purpose of this article is to present a methodology for calculating the aging rate of a storage battery inserted in a hybrid multisource system. The approach consists in first knowing the solicitations of the battery during a year knowing at every moment its state of charge. This curve is obtained from a dynamic simulator taking into account the intermittences of the sources and the load. The second step is to determine the number of cycles and the depth of discharge of each from the stat of charge. Finally, based on the battery life characteristic given by the manufacturer (cycle number vs. discharge depth), the aging rate of the battery for one year of operation is determined.</p>

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.

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.

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