The increasing deployment of battery storage applications in both grid storage and electric vehicle fields is generating a vast used battery market. These batteries are typically recycled but could be reused in second-life applications. One of the challenges is to obtain an accurate remaining useful life (RUL) estimation algorithm, which determines whether a battery is suitable for reuse and estimates the number of second-life cycles the battery will last. In this article, the RUL estimation problem is considered. We propose several health indicators (HIs), some of which have not been explored before, along with simple yet effective estimation and classification algorithms. These algorithms include classification techniques such as regularized logistic regression (RLR), and regression techniques such as multivariable linear regression (MLR) and multilayer perceptron (MLP). As a more advanced solution, a multiple expert system combining said techniques is proposed. The performance of the algorithms and features is evaluated on a recent lithium iron phosphate (LFP) data set from Toyota Research Institute. We obtain satisfactory results in the estimation of RUL cycles with errors down to 49 root mean square error (RMSE) cycles for cells that live up to 1200 cycles, and 0.24% mean relative error (MRE) for the prediction of the evolution of capacity.

The use of energy storage systems in standalone photovoltaic installations is essential to supply energy demands, independently of solar generation. Accurate prediction of the battery state is critical for the safe, durable, and reliable operation of systems in this type of installations. In this study, an installation located in the area of Aragon (Spain) has been considered. Two methods, based on different types of Recurrent Neural Networks (RNN), are proposed to predict the battery voltage of the installation two days ahead. Specifically, the Nonlinear Auto Regressive with Exogenous Input (NARX) network and the Long Short-Term Memory (LSTM) network are studied and compared. The implemented algorithms process battery voltage, temperature and current waveforms; and rely on the selection of different future scenarios based on weather forecasting to estimate the future voltage of the battery. The proposed methodology is capable of predicting the voltage with a Root Mean Squared Error (RMSE) error of 1.2 V for batteries of 48 V, in critical situations where the installation is running out of energy. The study contributes to the ongoing research of developing preventive control systems that help reduce costs and improve the performance of remote energy storage systems based on renewable energies with a positive outcome.

The second life use of lithium-ion batteries has gained significant attention in recent years, driven by the potential to repurpose cells from electric vehicles for less demanding applications. A critical aspect of this repurposing is accurately estimating the Remaining Useful Life (RUL) of the batteries. Traditional techniques often rely on data from the battery’s first life, which may not be available in practical scenarios. To address this issue, we propose a data-driven method for RUL estimation that does not depend on first-life information. Our approach considers a realistic scenario where an aged battery cell, lacking previous usage data, is evaluated for second life use through a limited number of test cycles. We compute features such as incremental capacity curves, and other health indicators from the measured voltage and current waveforms of the used cell. These features are automatically processed by deep learning algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. This methodology achieves an average error of only 62 cycles for cells with a lifespan of up to 1200 cycles and a RUL error of less than 10% for deeply aged batteries. These results outperform state-of-the-art algorithms that utilize data from the cell’s entire lifespan, demonstrating the efficacy and robustness of this approach.

This paper explores a new methodology based on data-driven approaches to identify and track degradation processes in Li-ion batteries. Our goal is to study if it is possible to differentiate the state of degradation of cells that present similar aging in terms of overall parameters (similar remaining capacity, state of health or internal resistance), but that have had different applications or conditions of use (different discharge currents, depth of discharges, temperatures, etc.). For this purpose, this study proposed to analyze voltage waveforms of cells obtained in cycling tests by using an unsupervised neural network, the Self-Organizing Map (SOM). In this work, a laboratory dataset of real Li-ion cells was used, and the SOM algorithm processed battery cell features, thus carrying out smart sensing of the battery. It was shown that our methodology differentiates the previous conditions of use (history) of a cell, complementing conventional metrics such as the state of health, which could be useful for the growing second-life market because it allows for determining more precisely the state of disease of a battery and assesses its suitability for a specific application.

Energy Storage in photovoltaic installations has increased in popularity in recent years due to the improvement in solar panel technology and energy storage systems. In several places where the grid is not available, in remote isolated rural locations or developing countries, isolated photovoltaic installations are one of the main options to power DC micro-grids. In these scenarios, energy storage elements are mandatory due to the natural day-night cycles and low irradiation periods. Traditionally, lead-acid batteries have been responsible for this task, due to their availability and low cost. However, the intermittent features of the solar irradiance patterns and load demand, generate multiple shallow charge–discharge cycles or high power pulses, which worsen the performance of these batteries. Some Hybrid Energy Storage Systems (HESSs) have been reported in the literature to enhance the lifetime and power capabilities of these storage elements, but they are not intended to overcome the Partial State of Charge (PSoC) issue caused by daily cycles, which has an effect on the short and mid-term performance of the system. This paper studies the impact of the already proposed HESSs on PSoC operation, establishing the optimal hybrid ratios, and implementing them in a real installation with a satisfactory outcome.