Global warming, pollution, and extreme weather events underscore the urgent need for renewable energy solutions, making efficient battery storage systems essential. This study investigates the implementation of a hybrid energy storage system (HESS) comprising three lithium-ion batteries—two nickel manganese cobalt (NMC) and one lithium titanate oxide (LTO)—to meet power demands while optimizing performance and lifespan. Two equivalent circuit models (ECMs) were developed based on experimental data, and nonlinear model predictive control (NLMPC) was employed to balance current, state of charge (SOC), and temperature through weighted constraints. This approach ensures reliable power delivery and prevents the violation of operational thresholds. The proposed system demonstrates improved battery longevity and efficiency across various test scenarios, offering a broad spectrum of performance outcomes.

This paper presents a simulation-based analysis of a Hybrid Battery Energy Storage System (HBESS) that combines a commercial nickel manganese cobalt (NMC811) cell with a next-generation cell featuring Silicon-Graphite (SiGr) and lithium nickel manganese oxide (LNMO) electrodes. Using two Pseudo Two-Dimensional (P2D) physics-based models, one for each cell in the HBESS, the system’s performance and degradation are evaluated. The work begins by outlining the current Lithium-Ion Battery (LIB) landscape and emphasizing emerging chemistries. The methodology used to calibrate the P2D model of the LNMO–SiGr cell is also described and validated against experimental full-cell data. A real-world stationary energy storage scenario is simulated to assess the benefits of hybridization. The proposed HBESS architecture reduces the cobalt content of the battery and mitigates degradation risks associated with silicon expansion by operating the LNMO–SiGr cell within a controlled 80–20% State of Charge (SOC) window. The NMC811 cell manages the system’s base load, ensuring stability, while the LNMO–SiGr cell provides supplementary energy during peak demands. This hybrid approach has the potential to enhance system durability, sustainability, and overall performance by leveraging the strengths of different lithium-ion chemistries.

The aggravation of the circumstances of climate change and environmental degradation highlights the importance of renewable energy systems, with advanced battery storage playing a pivotal role. This analysis presents a hybrid energy storage system (HESS) integrating three lithium-ion batteries—two nickel manganese cobalt (NMC) and one lithium titanate oxide (LTO)—designed to meet dynamic power demands while enhancing system durability and efficiency. The complete system includes, for the power electronic, multi-port DC-DC converters, which offers a bidirectional controlled power flow capability for a better power transfer. The development of two equivalent circuit models (ECMs) is achieved from experimental data, which are incorporated into a nonlinear model predictive control (NLMPC) framework. The controller strategically regulates current distribution and state of charge (SOC) through constraints to ensure reliable power delivery and prevent violation of operational thresholds while maximizing its performance. Simulation results confirms the potential of predictive control and HESS in future-oriented energy storage solutions.

This conference paper explores hybrid battery energy storage systems (HBESS) through a simulation-based analysis, combining a commercial nickel manganese cobalt oxide cell with a next-generation cell and evaluating their performance under a realistic application scenario. The study employs physics-based models (PBMs) to simulate and estimate system performance. The paper begins with an overview of actual lithium-ion battery technologies, providing context on the research landscape, with a focus on cobalt-free and graphite-silicon electrodes. It then outlines the PBM used, followed by the parameterization of the cells forming the HBESS. A detailed description is provided of the steps undertaken to calibrate the next-generation cell model, which has been validated using experimental full-cell data. The article further discusses key aspects of the hybrid system, including the application of model predictive control strategies and critical operational considerations. A case scenario, based on an electric vehicle usage pattern, is then evaluated through simulation to assess the potential benefits in terms of performance optimization and degradation mitigation. In this way, the study demonstrates how hybridization strategies can leverage the strengths of different battery technologies when integrated with actual battery applications, reducing degradation and cobalt content in battery packs.

This conference paper presents a comparative study of various models used to characterize the behavior of NMC Li-ion batteries. Specifically, this work focuses on analyzing data granularity and examining important features such as model accuracy and required computing resources. The paper presents the aforementioned study employing two different types of models: a physics-based model found in the literature and the well-known equivalent circuit model. Each of these models are formulated and calibrated using different techniques based on their typology. To conduct these studies, a real NMC811 lithium-ion battery with a 5Ah capacity is considered. A set of charging and discharging profiles were used to analyze and compare the results obtained from different models.

This conference paper presents a comparison study between different charging techniques for energy storage systems. The work presents the application of charging methods in two different types of models, which are a dynamic nonlinear electrochemical and the well-known equivalent circuit model. For both cases, a controller is designed in order to analyze its performance, using the classical PID implemented in the vast majority of industry controllers. In order to validate its implementation, the case of an emerging technology in terms of energy storage has been considered, as is the vanadium redox flow battery. The models have been calibrated for later validation, using a particle swarm optimizer and a real dataset found in the literature. The controllers have been developed separately, considering the variables and characteristics of each model. Finally, a comparison of both controlled systems is presented.

Energy storage systems can provide a solution for the current challenges derived from the increasing penetration of renewable energies. Each energy storage system has different characteristics so their combination can be the best solution to achieve the requirements of a given scenario. To achieve the maximum potential of the Energy storage system they must be supplied with an optimal control strategy. Traditional control strategies only focus on increasing self consumption and do not take into consideration future generation and load. Model predictive control can use load and generation forecasts to provide a multi-objective solution which takes into consideration energy storage system degradation, grid congestion and self consumption between others. Neural networks are used to obtain the generation and load forecast, trained with empirical data from real households. An online model based predictive controller implemented for a grid composed by one lithium-ion battery, one vanadium redox flow battery, photovoltaic generation and electric consumption of 14 households. Finally the results of the classical method of maximizing self consumption, the ideal predictive controller considering perfect forecast and the real predictive controller are shown and discussed.

This conference paper presents a comparison between a$H_{\infty}$control technique and a classical PID, applied on a redox flow battery system. The study presents a dynamic nonlinear electrochemical model that considers the effect of over-potentials losses in the computation of the voltage measurement. The controller is designed to regulate the output voltage of the battery. The$H_{\infty}$controller is designed using the classical weighting function approach while the PID controller is tuned using a particle swarm optimizer. Finally, a comparison between the designed$H_{\infty}$controller, and the classic PID is presented.