Battery models have gained great importance in recent years, thanks to the increasingly massive penetration of electric vehicles in the transport market. Accurate battery models are needed to evaluate battery performances and design an efficient battery management system. Different modeling approaches are available in literature, each one with its own advantages and disadvantages. In general, more complex models give accurate results, at the cost of higher computational efforts and time-consuming and costly laboratory testing for parametrization. For these reasons, for early stage evaluation and design of battery management systems, models with simple parameter identification procedures are the most appropriate and feasible solutions. In this article, three different battery modeling approaches are considered, and their parameters’ identification are described. Two of the chosen models require no laboratory tests for parametrization, and most of the information are derived from the manufacturer’s datasheet, while the last battery model requires some laboratory assessments. The models are then validated at steady state, comparing the simulation results with the datasheet discharge curves, and in transient operation, comparing the simulation results with experimental results. The three modeling and parametrization approaches are systematically applied to the LG 18650HG2 lithium-ion cell, and results are presented, compared and discussed.

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In the energy storage field, supercapacitors (SCs) are gaining more and more attention thanks to features such as highpower density, high life cycles and lack of maintenance. In this article, an improved SC three-branches model which considers the residual charge phenomenon is presented. The procedure to estimate the model parameters and the related experimental set-up are presented. The parameter estimation procedure is repeated for several SCs of the same type. The average parameters are then obtained and used as initial guesses for a recursive least square optimization algorithm, to increase the accuracy of the model. The model of a single SC is then extended to SC banks, testing different configurations and operating conditions. The simulation results, obtained in Matlab/Simulink environment for both the single SC and the different SC bank configurations, are validated with experimental tests to assess the model accuracy.

No abstract

In the energy storage field, supercapacitors (SCs) are gaining more and more attention thanks to features such as highpower density, high life cycles and lack of maintenance. In this article, an improved SC three-branches model which considers the residual charge phenomenon is presented. The procedure to estimate the model parameters and the related experimental set-up are presented. The parameter estimation procedure is repeated for several SCs of the same type. The average parameters are then obtained and used as initial guesses for a recursive least square optimization algorithm, to increase the accuracy of the model. The model of a single SC is then extended to SC banks, testing different configurations and operating conditions. The simulation results, obtained in Matlab/Simulink environment for both the single SC and the different SC bank configurations, are validated with experimental tests to assess the model accuracy.