• Energy Research

The unpredictable nature of renewable energies is drawing attention to lithium-ion batteries. In order to make full utilization of these batteries, some research works are focused on the management of existing systems, while others propose sizing techniques based on business models. However, in order to optimise the global system, a comprehensive methodology that considers both battery sizing and management at the same time is needed. This paper proposes a new optimisation algorithm based on a combination of dynamic programming and a region elimination technique that makes it possible to address both problems at the same time. This is of great interest, since the optimal size of the storage system depends on the management strategy and, in turn, the design of this strategy needs to take account of the battery size. The method is applied to a real installation consisting of a 100 kWp rooftop photovoltaic plant and a Li-ion battery system connected to a grid with variable electricity price. Results show that, unlike conventional optimisation methods, the proposed algorithm reaches an optimised energy dispatch plan that leads to a higher net present value. Finally, the tool is used to provide a sensitivity analysis that identifies key informative variables for decision makers The authors would like to acknowledge the support of the Spanish State Research Agency and FEDER-UE under grants DPI2016-80641-R and DPI2016-80642-R; of Government of Navarra through research project PI038 INTEGRA-RENOVABLES; and the FPU Program of the Spanish Ministry of Education, Culture and Sport (FPU13/00542).

An increasing number of applications with diverse requirements incorporate various battery technologies. Selecting the most suitable battery technology becomes a tedious task as several aspects need to be taken into account. Two of the key aspects are the battery characteristics under temperature variations and their degradation. While numerous contributions using tailored assessment methods to evaluate both aspects for a particular application exist in the literature, a general methodology for analysis is necessary to enable a quantitative comparison between different technologies. We propose in this paper a novel methodology, based on performance indicators, to quantify the potential and limitations of a battery technology for diverse applications sharing a similar operational profile. A quantification of phenomena such as the influence of high and low temperatures on the battery, or the effect of cycling and state of charge on battery aging is obtained. In pursuit of these indicators, an experimental procedure and the fitting of aging model parameters that allow their calculation are proposed. As an additional outcome of this work, a general aging model that allows comprehensive analysis of aging behavior is developed and the trade-off between experimental time and accuracy is analyzed to find an optimal experimental time between 2 and 4 months, depending on the studied battery technology. Finally, the proposed methodology is applied to four battery technologies in order to show its potential in a real case-study.

Nowadays, electric vehicle batteries reutilization is considered such as a feasible alternative to recycling, as it allows to benefit from their remaining energy and to enlarge their lifetime. Stationary applications as self-consumption or isolated systems support are examples of possible second life uses for these batteries. However, the modules that compose these batteries have very heterogeneous properties, and therefore condition their performance. This paper aims to characterize and analyze the existing capacity dispersion of Nissan Leaf modules that have reached the end of their lifetime on their original application and of new modules of this Electric Vehicle, in order to establish a comparison between them. The authors would like to acknowledge the support of the Spanish State Research Agency (AEI) and FEDER-UE under grants DPI2016-80641-R and DPI2016-80642-R and of Government of Navarra through research projects PI020 RENEWABLE STORAGE and 0011-1411-2018-000029 GERA.

Experimental aging studies are commonly conducted on lithium-ion batteries by full charge and discharge cycles. However, such profiles may differ from the actual operation of batteries in electric vehicles and stationary applications, where they are subjected to different partial charges and discharges. These partial cycles, which take place during a main charge or discharge process, are called micro-cycles if their depth of discharge is 1500. Nevertheless, if the number of deep cycles, disregarding micro-cycles, is the unit to measure battery use, then the degradation of cells with and without micro-cycles is similar. Based on this result, the number of cycles can be identified as a more accurate variable to measure the use of a cell, in comparison to charge throughput. This work is part of the projects PID2019-111262RB-I00, funded by MCIN/AEI/10.13039/501100011033/, STARDUST (774094), funded by European Union¿s Horizon 2020 research and innovation programme, HYBPLANT (0011-1411-2022-000039), funded by Government of Navarre, and a Ph.D. scholarship, funded by Public University of Navarre. Open access funding provided by Universidad Pública de Navarra.

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. The authors would like to acknowledge the support of the Spanish State Research Agency (AEI) and FEDER-UE under grants DPI2016-80641-R and DPI2016-80642-R and of Government of Navarra through research projects PI020 RENEWABLE-STORAGE and 0011-1411-2018-000029 GERA.

This paper proposes an energy management strategy for a residential microgrid comprising photovoltaic (PV) panels, a small wind turbine and solar thermal collectors. The microgrid can control the power exchanged with the grid thanks to a battery and a controllable electric water heater, which provide two degrees of freedom to the control strategy. As input data, the proposed control strategy uses the battery state of charge (SOC), the temperature of the hot water tank, the power of each microgrid element as well as the demand and renewable generation forecasts. By using forecasted data and by controlling the electric water heater, the strategy is able to achieve a better grid power profile while using a smaller battery than previous works, hence reducing the overall cost of the system. The strategy is tested by means of simulation with real data for one year and it is also experimentally validated in the microgrid built at the Renewable Energy Laboratory at the UPNA. The authors would like to acknowledge that this work has been partially funded under grants PID2019-110816RB-C21 and PID2019–111262RB–I00 by the Spanish State Research Agency (AEI/ 10.13039/501100011033). And also by VLIR-UOS and the Belgian Development Cooperation (DGD) under the project EC2020SIN322A101 (2020-EXT-007).

AbstractThe relative weight of the energy generated by means of renewable sources is constantly increasing. Among all these sources, the photovoltaic (PV) systems present the higher and more stable relative growth. However, the PV system is still too expensive and a significant effort is being done to increase the efficiency and reduce the cost. Concerning the PV inverters, this has lead to the elimination of the low frequency (LF) transformer that has been traditionally included. The LF transformer provides isolation from the grid but reduces the PV inverter efficiency and increases its size and cost. However, the elimination of the transformer might generate strong ground currents, which become now an important design parameter for the PV inverter. The ground currents are a function of the system stray elements. However, there is no simple model and procedure to study the common mode behavior of a PV system, which is required to analyze the ground currents. In this paper, a comprehensible model is proposed which provides a better understanding of the common mode issue in single‐phase transformerless PV systems. In addition, a procedure is developed to analyze the global performance, efficiency, grid current quality, and common mode behavior of a PV inverter as a function of its particular structure and modulation technique. Copyright © 2007 John Wiley & Sons, Ltd.

Nowadays, the economic viability of second-life (SL) Li-ion batteries from electric vehicles is still uncertain. Degradation assessment optimization is key to reduce costs in SL market not only at the repurposing stage, but also during SL lifetime. As an indicator of the ageing condition of the batteries, state of health (SOH) is currently a major research topic, and its estimation has emerged as an alternative to traditional characterization tests. In an initial stage, all SOH estimation methods require the extraction of health indicators (HIs), which influence algorithm complexity and on-board implementation. Nevertheless, a literature gap has been identified in the assessment of HIs for reused Li-ion batteries. This contribution targets this issue by analysing 58 HIs obtained from incremental capacity analysis, partial charging, constant current and constant voltage stage, and internal resistance. Six Nissan Leaf SL modules were aged under extended cycling testing, covering a SOH range from 71.2 % to 24.4 %. Results show that the best HI at the repurposing stage was obtained through incremental capacity analysis, with 0.2 % of RMSE. During all SL use, partial charge is found to be the best method, with less than 2.0 % of RMSE. SOH is also estimated using the best HI and different algorithms. Linear regression is found to overcome more complex options with similar estimation accuracy and significantly lower computation times. Hence, the importance of analysing and selecting a good SL HI is highlighted, given that this made it possible to obtain accurate SOH estimation results with a simple algorithm. This work is part of the projects PID2019-111262RB-I00, funded by MCIN/AEI/10.13039/501100011033/, STARDUST (774094), funded by European Union's Horizon 2020 research and innovation programme, HYBPLANT (0011-1411-2022-000039), funded by Government of Navarre, and a Ph.D. scholarship, also funded by Government of Navarre. Open access funding provided by Universidad Pública de Navarra.