Accurate and robust battery models are required for the proper design and operation of battery-powered systems. However, the parametric identification of these models requires extensive and sophisticated methods to achieve enough accuracy. This paper shows a general and straightforward procedure, based on Simulink and Simscape of Matlab®, to build and parameterize Li-ion battery models. The model parameters are identified with the Optimization Toolbox of Matlab®, by means of an iterative process to minimize the sum of the squared errors. In addition, this procedure is applied to a selection of five different models available in the literature for electric vehicle applications, obtaining a comparative study between them. Also, the performance of each battery model is evaluated through two current profiles from two driven profiles known as the Urban Driving Cycle (ECE-15 or UDC) and the Hybrid Pulse Power Characterization (HPPC). The experimental results obtained from a Li-ion polymer battery have been compared with the data provided by the models, confirming the effectiveness of the proposed procedure, and also, the application field of each model as a function of the required accuracy.

Supercapacitors with characteristics such as high power density, long cycling life, fast charge, and discharge response are used in different applications like hybrid and electric vehicles, grid integration of renewable energies, or medical equipment. The parametric identification and the supercapacitor model selection are two complex processes, which have a critical impact on the system design process. This paper shows a comparison of the six commonly used supercapacitor models, as well as a general and straightforward identification parameter procedure based on Simulink or Simscape and the Optimization Toolbox of Matlab^®. The proposed procedure allows for estimating the different parameters of every model using a different identification current profile. Once the parameters have been obtained, the performance of each supercapacitor model is evaluated through two current profiles applied to hybrid electric vehicles, the urban driving cycle (ECE-15 or UDC) and the hybrid pulse power characterization (HPPC). The experimental results show that the model accuracy depends on the identification profile, as well as the robustness of each supercapacitor model. Finally, some model and identification current profile recommendations are detailed.

The fuel cell based vehicles powertrain is an extensive system that comprises a fuel cell (FC) as the primary energy source, a set of power converters both unidirectional and bidirectional and batteries or supercapacitors as secondary energy sources. Its design is a complex task that affects the mass, volume, cost, efficiency and fuel economy of the vehicle. This paper describes a graphic and straightforward sizing of the secondary energy sources needed to fulfil the vehicle load requirements, as well as the set of equations related to the mass, cost and volume of each one of the power distribution subsystems. Moreover, this paper analysis ten different power distribution architectures to conclude which is the most suitable secondary energy source, the minimum sizing, cost, volume and weight, depending on the amount of power delivered by the fuel cell. Also, a 1.6 kW fuel cell based architecture is implemented and testing. The experimental results confirm the proposed methodology.

Energy storage devices such as batteries and supercapacitors are arousing a growing interest in many areas of industrial applications since they allow improving the dynamic response and efficiency. In this sense, the availability of electric models of batteries and supercapacitors is necessary for the correct sizing, control, and energy management of power systems. In order to identify the parameters of the equivalent electric model, the identification current profile is often a pulse of charge or discharge. Subsequently, these models are validated with a current verification profile. However, the verification profile compared to the identification profile is more dynamic in amplitude and frequency, producing validation errors due to the identification signal's limited bandwidth. This paper proposes a novel Pulse – Pseudo Random Binary Sequence (Pulse – PRBS) identification current profile, with similar spectral characteristics to the verification profile, in order to improve the modeling accuracy. The parameters of a Lithium-ion battery and a supercapacitor are obtained with this new identification current profile and the Optimization Toolbox of Matlab, using the electrical models in Simulink or Simscape. Finally, the models are validated with the Urban Driving Cycle ECE-15 and the Hybrid Pulse Power Characterization (HPPC) for electric vehicle applications.