Over the coming years, major growth in the use of Li-ion batteries is expected, both in electric mobility as well as in stationary applications, be it in self-consumption systems and micro grids or in large renewable power generation plants. The proper characterization of lithium-ion cells is of vital importance for the development of precise models that permit the simulation and prediction of their behavior, so as to suitably configure cell groupings for the resulting battery packs, and to properly select the most suitable cells from the extensive manufacturer offer. In this work, an analysis is conducted of the main techniques used in the literature to characterize batteries. Also, an experimental comparative is carried out on 18650 Liion cells from three large global manufacturers, focusing on the primary methodologies used to characterize capacity, internal resistance and open circuit voltage. Finally, the advantages and disadvantages are presented for the methodologies used, based on the experimental results obtained.

Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. For a proper use of such storage systems, an energy management algorithm (EMA) is required. A number of EMAs, with various characteristics have been published recently, given the diverse nature of battery problems. The EMA of deterministic battery problems is usually based on an optimization algorithm. The selection of such algorithm depends on a few problem characteristics, that need to be identified and closely analyzed. The aim of this paper is to identify the critical optimization problem parameters that determine the most suitable EMA for a Li-ion battery. With this purpose, the starting point is a detailed model of a Li-ion battery. Three EMAs based on the algorithms used to face deterministic problems, namely dynamic, linear and quadratic programming, are designed to optimize the energy dispatch of such battery. Using real irradiation and power price data, the results of these EMAs are compared for various case studies. Given that none of the EMAs achieves the best results for all analyzed cases, the problem parameters that determine the most suitable algorithm are identified to be four: (i) desired computation intensity, (ii) characteristics of the battery aging model, (iii) battery energy and power capabilities and (iv) number of optimization variables, which are determined by the number of energy storage systems, the length of the optimization problem and the desired time step.

In the last few years, the growing demand for electric vehicles (EVs) in the transportation sector has contributed to the increased use of electric rechargeable batteries. At present, lithium-ion (Li-ion) batteries are the most commonly used in electric vehicles. Although once their storage capacity has dropped to below 80–70% it is no longer possible to use these batteries in EVs, it is feasible to use them in second-life applications as stationary energy storage systems. The purpose of this study is to present an embedded system that allows a Nissan^® LEAF Li-ion battery to communicate with an Ingecon^® Sun Storage 1Play inverter, for control and monitoring purposes. The prototype was developed using an Arduino^® microcontroller and a graphical user interface (GUI) on LabVIEW^®. The experimental tests have allowed us to determine the feasibility of using Li-ion battery packs (BPs) coming from the automotive sector with an inverter with no need for a prior disassembly and rebuilding process. Furthermore, this research presents a programming and hardware methodology for the development of the embedded systems focused on second-life electric vehicle Li-ion batteries. One second-life battery pack coming from a Nissan^® Leaf and aged under real driving conditions was integrated into a residential microgrid serving as an energy storage system (ESS).

This article presents a noninvasive technical analysis of the degradation of four lithium-ion batteries (LIBs) used in extreme frigid weather. In contrast to other studies in which the batteries were tested in laboratory conditions, the LIBs studied in this article were aged in a real application, more specifically in the WindSled project. In this project, an expedition was made using a zero-emission vehicle drawn by kites, covering more than 2500 km on the East Antarctic Plateau. The study performed in this article aims to quantify the degradation of the LIBs during the expedition. The results show a 5% capacity fade, a 30% increase in the internal resistance and no substantial increase in the impedance of the solid electrolyte interface. Moreover, no evidence of dendrite growth at the anode is inferred by the interpretation of the distribution of relaxation times, incremental capacity analysis, and differential voltage analysis. Based on these results, it can be claimed that the LIBs used in the WindSled Project can successfully operate under – 50 °C. Furthermore, since noninvasive techniques were used to characterize the batteries, they can still be used in upcoming expeditions, with subsequent financial and environmental benefits.

This contribution analyses lifetime estimation errors due to the effect of power fluctuations in lithium-ion batteries connected to microgrids when different time steps are used for the calculations. Usually, not every second data are available or the computational cost is excessively high. Those facts result in the use of larger time steps. However, the increase of the time steps may turn out in too optimistic predictions. Data from a real microgrid make it possible to optimize calculation times while keeping low errors. The results show that when 1 minute time step is set, the computation time is reduced by 14.4 times while the lifetime overstatement is only 3.5-5.2% higher, depending on the aging model.

Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. A good energy management strategy is required in order to increase the profitability of an energy system using a Li-ion battery for storage. The vast number of management algorithms that has been proposed to optimize the achieved profit, with diverse computational power requirements and using models with different complexity, raise doubts about the suitability of an algorithm and the required computation power for a particular application. The performance of three energy management algorithms based on linear, quadratic, and dynamic programming are compared in this work. A realistic scenario of a medium-sized PV plant with a constraint of peak shaving is used for this comparison. The results achieved by the three algorithms are compared and the grounds of the differences are analyzed. Among the three compared algorithms, the quadratic one seems to be the most suitable for renewable energy applications, given the undue simplification of the battery aging required by the linear algorithm and the discretization and computational power required by a dynamic algorithm.