Publications resulting from the project
Publications resulting from the project
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de la Vega, J.; Riba, J.-R.; Ortega, J.A. Applied Sciences 2023, 13(8), 4938. https://doi.org/10.3390/app13084938
Abstract
This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is calculated within a narrow SOC interval where the voltage vs. SOC relationship is very linear and that is within the usual transit range for most practical charge and discharge cycles. As a result, only a small fraction of the data points of a full charge–discharge cycle are required, reducing storage and computational resources while providing accurate results. Finally, by using the battery model defined by the Nernst equation, the behavior of future charge–discharge cycles can be accurately predicted, as shown by the results presented in this paper. The proposed approach requires the application of appropriate signal processing techniques, from discrete wavelet filtering to prediction methods based on linear fitting and autoregressive integrated moving average algorithms.
Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods
de la Vega, J.; Riba, J.-R.; Ortega, J.A. Batteries 2024, 10(1), 10. https://doi.org/10.3390/batteries10010010
Abstract
Lithium-ion batteries are key elements in the development of electrical energy storage solutions. However, due to cycling, environmental, and operating conditions, battery capacity tends to degrade over time. Capacity fade is a common indicator of battery state of health (SOH) because it is an indication of how the capacity has been degraded. However, battery capacity cannot be measured directly, and thus, there is an urgent need to develop methods for estimating battery capacity in real time. By analyzing the historical data of a battery in detail, it is possible to predict the future state of a battery and forecast its remaining useful life. This study developed a real-time, simple, and fast method to estimate the cycle capacity of a battery during the charge cycle using only data from a short period of each charge cycle. This proposal is attractive because it does not require data from the entire charge period since batteries are rarely charged from zero to full. The proposed method allows for simultaneous and accurate real-time prediction of the health and remaining useful life of the battery over its lifetime. The accuracy of the proposed method was tested using experimental data from several lithium-ion batteries with different cathode chemistries under various test conditions.
From Present Innovations to Future Potential: The Promising Journey of Lithium-Ion Batteries
Parvizi, P.; Jalilian, M.; Amidi, A.M.; Zangeneh, M.R.; Riba, J.-R. Micromachines 2025, 16(2), 194. https://doi.org/10.3390/mi16020194
Abstract
Lithium-ion batteries (LIBs) have become integral to modern technology, powering portable electronics, electric vehicles, and renewable energy storage systems. This document explores the complexities and advancements in LIB technology, highlighting the fundamental components such as anodes, cathodes, electrolytes, and separators. It delves into the critical interplay of these components in determining battery performance, including energy density, cycling stability, and safety. Moreover, the document addresses the significant sustainability challenges posed by the widespread adoption of LIBs, focusing on resource depletion and environmental impact. Various recycling practices, including hydrometallurgy, pyrometallurgy, and direct recycling, are evaluated for their efficiency in metal recovery and ecological footprint. The advancements in recycling technologies aim to mitigate the adverse effects of LIB waste, emphasizing the need for sustainable and scalable solutions. The research underscores the importance of ongoing innovation in electrode materials and recycling methodologies, reminding us of our responsibility and commitment to finding and implementing these solutions, as this continuous improvement is crucial to enhance the performance, safety, and sustainability of LIBs, ensuring their continued relevance in the evolving energy storage landscape.
de la Vega, J.; Riba, J.-R.; Ortega, J.A. Applied Sciences 2025, 15(20), 11291. https://doi.org/10.3390/app152011291
Abstract
Lithium-ion batteries are key elements in the development of electrical energy storage solutions. However, due to cycling, environmental, and operating conditions, battery capacity tends to degrade over time. Capacity fade is a common indicator of battery state of health (SOH) because it is an indication of how the capacity has been degraded. However, battery capacity cannot be measured directly, and thus, there is an urgent need to develop methods for estimating battery capacity in real time. By analyzing the historical data of a battery in detail, it is possible to predict the future state of a battery and forecast its remaining useful life. This study developed a real-time, simple, and fast method to estimate the cycle capacity of a battery during the charge cycle using only data from a short period of each charge cycle. This proposal is attractive because it does not require data from the entire charge period since batteries are rarely charged from zero to full. The proposed method allows for simultaneous and accurate real-time prediction of the health and remaining useful life of the battery over its lifetime. The accuracy of the proposed method was tested using experimental data from several lithium-ion batteries with different cathode chemistries under various test conditions.
Advanced Battery Testbench For Realistic Vehicle Driving Conditions Assessment
de la Vega, J.; Riba, J.-R.; Ortega, J.A. 2025 IEEE 19th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), Antalya, Turkiye, 2025, pp. 1-6. https://doi.org/10.1109/CPE-POWERENG63314.2025.11027228
Abstract
This study presents a battery test bench, adaptable to different battery types, designed to replicate real-world vehicle operating conditions but also constant charge and discharge configurations. It incorporates on-board communication via the Controller Area Network (CAN). Unlike conventional battery test setups, which typically focus on single cells and constant current profiles, this test bench evaluates battery packs under dynamic current loads, allowing simulation of real-world driving conditions such as the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) or other driving cycles. The test bench also has an ambient temperature control from 5∘C to 60∘C. It provides accurate data acquisition of cell voltage and current, cell and ambient temperature, and resistance at multiple levels - pack, string, and individual cells for a more complete understanding of battery behavior. The system is controlled by a Python-based code that enables real-time monitoring and automation. The platform enables improved battery management system (BMS) validation, energy optimization strategies, and overall performance analysis. Although there are many battery cycling databases in the literature, there is a lack of databases that address the conditions that batteries must endure during realistic driving cycles.
Battery Pack Data Imputation Technique For Dynamic Profiles
de la Vega, J.; Riba, J.-R.; Ortega, J.A. IECON 2025 conference of the IEEE Industrial Electronics Society, Madrid, 14-17 October 2025 https://doi.org/10.1109/IECON58223.2025.11221106
Abstract
Lithium-ion batteries play a key role in electric mobility applications, where monitoring the health and behavior of individual cells is essential to ensure safety and performance over time. However, intermittent or persistent data gaps can arise due to several causes, such as sensor faults, communication breakdowns, or inadequate data acquisition infrastructure. Such missing data can disrupt the cell balancing process, leading to differences between cells and stress in specific cells, accelerating their degradation. This paper presents an imputation method based on the Unscented Kalman Filter (UKF) to reconstruct missing voltage measurements. The results presented here can help prevent early cell degradation and extend the useful life of the full battery pack.
Lithium-Ion Battery Pack Cycling Dataset with CC-CV Charging and WLTP/Constant Discharge Profiles
de la Vega, J.; Ortega, J.A.; Riba, J.-R. Sci Data 12, 1942 (2025) https://doi.org/10.1038/s41597-025-06229-5
Abstract
This work presents a database of a lithium-ion battery pack cycling tests generated from a customtest bench that simulates dynamic driving conditions based on the WLTP cycle. Current profiles werederived from speed-time data using MATLAB/Simulink and a Tesla Model 3 vehicle model. The datasetincludes time series data on cell voltages, currents, surface temperatures, and pack-level resistancefrom up to 36 cells arranged in three parallel branches. The data is recorded under controlled thermalconditions and stored in an efficient PARQUET format. The system uses a controller area network (CAN)bus architecture and commercial automotive battery management system (BMS) units to replicatein-vehicle communication constraints, enhancing the dataset’s relevance for real-world batterymanagement system development and validation.
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