Review on Lithium-Ion battery modeling for different applications

Battery modeling is one of the most important functions in a battery management system for different applications such as electrical vehicles, This article focuses on state of the art of lithium-ion battery modeling by exploring different existing modeling methods, such as Electrochemical models, Analytical models and the equivalent electrical circuit. First, the characteristics of the lithium-ion battery for different applications are reviewed ,we chose to study this type of battery because it offers satisfactory characteristics compared to other battery types, then the different modeling methods have been explored, finaly a conclusion with suggestion of other modeling type such as fractional order model have been proposed to improve efficiency and precision of battery management system

Review on Lithium-Ion battery modeling for different applications

Battery modeling is one of the most important functions in a battery management system for different applications such as electrical vehicles, This article focuses on state of the art of lithium-ion battery modeling by exploring different existing modeling methods, such as Electrochemical models, Analytical models and the equivalent electrical circuit. First, the characteristics of the lithium-ion battery for different applications are reviewed ,we chose to study this type of battery because it offers satisfactory characteristics compared to other battery types, then the different modeling methods have been explored, finaly a conclusion with suggestion of other modeling type such as fractional order model have been proposed to improve efficiency and precision of battery management system

An electric circuit model for a lithium-ion battery cell based on automotive drive cycles measurements

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