• Energy Research

Polymeric Electrolyte Membrane Fuel Cell (PEMFC) modeling considering thermal and electrical behavior in a coupled manner is a key aspect when evaluating new designs, materials, physical phenomena or control strategies. Depending on the behavior to be emulated, it is important to choose the modeling technique that best suits the needs required. In this sense, this paper describes the most commonly used PEMFC modeling techniques in the context of analytical-mechanistic approach, semi-empirical approach based on theoretical formulation and empirical correlations, as well as empirical approach based on experimentation with a real system. In addition, an in-depth analysis of PEMFC models at the cell and stack level that emulate the thermal and electrical behavior of these systems in a coupled manner is carried out. A chronological classification of the most relevant models has been made based on the modeling technique used, purpose of the model, state and dimension of the model, and the real system, other developed models or experimental results that have been used to validate the proposed new model. Additionally, guidelines to improve the energy efficiency of PEMFC systems through the development of new models are given. The authors thank the support from the Spanish Ministry of Economy, Industry and Competitiveness (project ENE2016-79145-R AEI/FEDER, UE), the Basque Government (project ELKARTEK KK-2017/00083 and GISEL research group IT1083-16), as well as from the University of the Basque Country UPV/EHU (project EHUA15/25 and research group funding PPG17/23).

The necessity of transport electrification is already undeniable due to, among other facts, global Greenhouse Gas (GHG) emissions and fossil-fuel dependency. In this context, electric vehicles (EVs) play a fundamental role. Such vehicles are usually seen by the network as simple loads whose needs have to be supplied. However, they can contribute to the correct operation of the network or a microgrid and the provision of ancillary services and delay the need to reinforce the power lines. These concepts are referred to as Vehicle-to-Grid (V2G), Vehicle-to-Building (V2B) and Vehicle-to-Home (V2H). In paper, a deep classification and analysis of published charging strategies is provided. In addition, optimal charging strategies must minimise the degradation of the batteries to increase their lifetime, since it is considered that the life of a battery ends when its capacity is reduced by 20% with respect to its nominal capacity. Therefore, an optimal integration of EVs must consider both grid and batteries impact. Finally, some guidelines are proposed for further research considering the current limitations of electric vehicle technology. Thus, these proposed guidelines are focused on V2G optimal management, enabling new business models while keeping economic viability for all parts involved.

Battery degradation is one of the key barriers to the correct deployment of electric vehicle technology. Therefore, it is necessary to model, with sufficient precision, the State of Health (SoH) of batteries at every moment to know if they are useful as well as to develop operating strategies aimed at lifetime maximization. This paper presents a commercial electric vehicle with a nickel-cobalt-manganese (NCM) battery cell model that is composed of electrical and degradation submodels given by cycling aging. The studied cell is an LG Chem E63 cell, which is used in Renault Zoe electric vehicles. This degradation model is based on experimental results that are interpolated in the Hermite Cubic Interpolation Polynomial (PCHIP), with the exception of the number of cycles, whose impact is determined by a potential law. Temperature and C-rate are found to be the most influential factors in the aging of these batteries. The degradation model developed presents an RMSE of 1.12% in capacity fade and 2.63% in power fade. Furthermore, an application of the model is presented, in which high demanding (highway), average demanding (mixed), and low demanding (urban) driving environments are analyzed in terms of degradation.

Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for traction, especially nickel-manganese-cobalt (NMC) and nickel-cobalt-aluminum (NCA). An appropriate model of these batteries is fundamental for the simulation of several processes inside an EV, such as the state of charge (SoC) estimation, capacity and power fade analysis, lifetime calculus, or for developing control and optimization strategies. There are different models in the current literature, among which the electric equivalent circuits stand out, being the most appropriate model when performing real-time simulations. However, impedance models for battery diagnosis are considered very attractive. In this context, this paper compares and contrasts the different electrical equivalent circuit models, impedance models, and runtime models for battery-based EV applications, addressing their characteristics, advantages, disadvantages, and usual applications in the field of electromobility. In this sense, this paper serves as a reference for the scientific community focused on the development of control and optimization strategies in the field of electric vehicles, since it facilitates the choice of the model that best suits the needs required.

This paper is focused on the development of a model for achieving optimal control of the cooling system of a polymer electrolyte membrane fuel cell (PEMFC)-based cogeneration system. This model is developed to help facilitate the development and application of control strategies to maximize the energy efficiencies of PEMFCs, so that the costs associated with electric and thermal generation can be reduced. The results of experimental analysis conducted using an actual PEMFC-based combined heat and power system that can produce 600 W of electrical power are presented. Then, the development and validation of a simulation model of the experimental system are discussed. This model is based on a combination of an artificial neural network (ANN) with a non-linear autoregressive exogenous configuration and a 3D lookup table (LUT) that updates the data input into the ANN as a function of the electrical power demand and the flow rate and input temperature of the coolant fluid. Due to the nonlinearity of the data contained in the 3D LUT, an algorithm based on linear interpolation and shape-preserving piecewise cubic Hermite dynamic functions is implemented to interpolate the data in 3D. As a result, the model can predict the outlet temperature of the coolant fluid and hydrogen consumption rate of the PEMFC as functions of the inlet temperature and flow rate of the coolant fluid and the electrical power demand. The proposed model exhibits high accuracy and can be used as a black box for the development of new optimization strategies. University of The Basque Country - UPV/EHU [UFI 11/28]