Search Results (25)

The transition to electric mobility has significantly increased the demand for lithium-ion batteries, raising concerns about their end-of-life management. Therefore, this study presents the design, development and first implementation steps of a stationary energy storage system utilizing second-life electric vehicle (EV) batteries. These batteries, no longer suitable for traction applications due to a reduced state of health (SoH) below 80%, retain sufficient capacity for less demanding stationary applications. The proposed system is designed to be flexible and scalable, serving both research and commercial purposes. Key challenges include heterogeneous battery characteristics, safety considerations due to increased internal resistance and battery aging, and the need for flexible power electronics. An optimized dual active bridge (DAB) converter topology is introduced to connect several batteries in parallel and to ensure efficient bidirectional power flow over a wide voltage range. A first prototype, rated at 50 $\mathrm{k}$$\mathrm{W}$, has been built and tested in the laboratory. This study contributes to sustainable energy storage solutions by extending battery life cycles, reducing waste, and promoting economic viability for industrial partners. Full article

Aqueous zinc-ion batteries (AZIBs) have emerged as promising candidates for large-scale energy storage due to their inherent safety, low cost, and environmental sustainability. However, in practical applications, AZIBs are constrained by the adverse reactions originating from the zinc anodes, including dendrite formation, hydrogen evolution reaction, corrosion, and passivation, which hinder their large-scale commercialization. Nowadays, alloying strategies have been recognized as efficient approaches to address these limitations and have gained significant attention. By introducing heterogeneous elements into Zn matrices, alloying strategies can suppress dendrite formation and side reactions, modulate the interfacial kinetic process, and enhance electrochemical stability. This review systematically discusses the advantages of alloying for Zn anodes, categorizes key design strategies, such as surface modifications, composite structures, functional alloying, gradient, and layered alloy designs, and meanwhile highlights their performance improvements. Furthermore, we suggest future directions for advanced alloy development, scalable fabrication design, and integrated system optimization. Alloy engineering represents a critical pathway toward high-performance, durable Zn anodes for next-generation AZIBs and other metal-ion batteries. Full article

The massive integration of variable renewable energy sources (RESs) poses the gradual necessity for new power system architectures with wide implementation of distributed battery energy storage systems (BESSs), which support power system stability, energy management, and control. This research presents a methodology and realization of a set of 11 BESS models based on different machine learning methods. The performance of the proposed models is tested using real-life BESS data, after which a comparative evaluation is presented. Based on the results achieved, a valuable discussion and conclusions about the models’ performance are made. This study compares the results of feedforward neural networks (FNNs), a homogeneous ensemble of FNNs, multiple linear regression, multiple linear regression with polynomial features, decision-tree-based models like XGBoost, CatBoost, and LightGBM, and heterogeneous ensembles of decision tree modes in the day-ahead forecasting of an existing real-life BESS in a PV power plant. A Bayesian hyperparameter search is proposed and implemented for all of the included models. Among the main objectives of this study is to propose hyperparameter optimization for the included models, research the optimal training period for the available data, and find the best model from the ones included in the study. Additional objectives are to compare the test results of heterogeneous and homogeneous ensembles, and grid search vs. Bayesian hyperparameter optimizations. Also, as part of the deep learning FNN analysis study, a customized early stopping function is introduced. The results show that the heterogeneous ensemble model with three decision trees and linear regression as main model achieves the highest average R² of 0.792 and the second-best nRMSE of 0.669% using a 30-day training period. CatBoost provides the best results, with an nRMSE of 0.662% for a 30-day training period, and offers competitive results for R²—0.772. This study underscores the significance of model selection and training period optimization for improving battery performance forecasting in energy management systems. The trained models or pipelines in this study could potentially serve as a foundation for transfer learning in future studies. Full article

Lithium-ion batteries (LIBs) are widely deployed in electric vehicles due to their high energy density and long cycle life. Even after retirement, typically at around 80% of their rated capacity, LIBs can still be repurposed for second-life applications such as residential energy storage systems (ESSs). However, effectively grouping these heterogeneous cells is crucial to optimizing performance of the module. Retired LIBs can be effectively repurposed for numerous second-life applications such as ESSs, and other power backups. In this paper, we compare four clustering approaches including random grouping, equal-number Support Vector Clustering, K-means, and an equal-number Gaussian Mixture Model (GMM) to organize 60 retired cells into 48 V modules consisting of 15-cell groups. We verify the performance of each method via simulations of a 15S2P configuration, focusing on the standard deviation of final charge voltage, average charge throughput, delta capacity, and coulombic efficiency. Based on the evaluation metrics analyzed after regrouping the battery cells and simulating them for second-life ESS applications, the GMM-based clustering method demonstrates better performance. Full article

This paper presents an analysis of energy use and sector coupling in a local energy community using a model based on energy cost centres (ECCs), functional units for decentralised responsibility and optimisation of energy use within defined system boundaries. The ECC model enables structured identification and optimisation of energy and material flows in complex industrial and urban settings. It was applied to a case study involving an energy-intensive steel plant and its integration with the surrounding community. The study assessed the potential for renewable electricity production (7914 MWh annually), green hydrogen generation, battery storage, and the reuse of 11,440 MWh of excess heat. These measures could offset 9598 MWh of grid electricity through local production and savings, reduce natural gas use by 4,116,850 Nm³, and lower CO₂ emissions by 10,984 tonnes per year. The model supports strategic planning by linking sectoral actions to measurable sustainability indicators. It is adaptable to data availability and stakeholder engagement, allowing both high-level overviews and detailed analysis of selected ECCs. Limitations include heterogeneous data sources, uneven stakeholder participation, and the need for refinement of sub-models. Nonetheless, the approach offers a replicable framework for integrated energy planning and supports the transition to sustainable, decentralised energy systems. Full article

Within the 21st century, in the Maritime Silk Road, wave energy, a clean renewable source, is drawing more interest, especially in areas with power shortages. This paper investigates wave energy in ships, particularly in a hybrid electric bulk carrier, by designing a system that supplements the existing power setup with oscillating buoy wave energy converters. The system includes diesel generators (DGs), a wave energy generation system, heterogeneous energy storage (consisting of battery storage (BS) and thermal storage (TS)), a combined cooling heat and power (CCHP) unit, and a power-to-thermal conversion (PtC) unit. To ensure safe and reliable navigation despite uncertainties in wave energy output, onboard power loads, and outdoor temperature, a robust coordination method is adopted. This method employs a two-stage robust optimization (RO) strategy to coordinate the various onboard units across different time scales, minimizing operational costs while satisfying all operational constraints, even in the worst-case scenarios. By applying constraint linearization, the robust coordination model is formulated as a mixed-integer linear programming (MILP) problem and solved using an efficient solver. Finally, the effectiveness of the proposed method is validated through case studies and comparisons with existing ship operation benchmarks, demonstrating significant reductions in operational costs and robust performance under various uncertain conditions. Notably, the simulation results for the Singapore–Trincomalee route show an 18.4% reduction in carbon emissions compared to conventional systems. Full article

Lithium-ion batteries are the core energy storage technology for electric vehicles and energy storage systems. Accurate state-of-charge (SOC) estimation is critical for optimizing battery performance, ensuring safety, and predicting battery lifetime. However, SOC estimation faces significant challenges under extreme temperatures and complex operating conditions. This review systematically examines the research progress on SOC estimation techniques over a wide temperature range, focusing on two mainstream approaches: model improvement and data-driven methods. The model improvement method enhances temperature adaptability through temperature compensation and dynamic parameter adjustment. Still, it has limitations in dealing with the nonlinear behavior of batteries and accuracy and real-time performance at extreme temperatures. In contrast, the data-driven method effectively copes with temperature fluctuations and complex operating conditions by extracting nonlinear relationships from historical data. However, it requires high-quality data and substantial computational resources. Future research should focus on developing high-precision, temperature-adaptive models and lightweight real-time algorithms. Additionally, exploring the deep coupling of physical models and data-driven methods with multi-source heterogeneous data fusion technology can further improve the accuracy and robustness of SOC estimation. These advancements will promote the safe and efficient application of lithium batteries in electric vehicles and energy storage systems. Full article

A peak shaving approach in selected industrial loads helps minimize power usage during high demand hours, decreasing total energy expenses while improving grid stability. A battery energy storage system (BESS) can reduce peak electricity demand in distribution networks. Quasi-dynamic load flow analysis (QLFA) accurately assesses the maximum loading conditions in distribution networks by considering factors such as load profiles, system topology, and network constraints. Achieving maximum peak shaving requires optimizing battery charging and discharging cycles based on real-time energy generation and consumption patterns. Seamless integration of battery storage with solar photovoltaic (PV) systems and industrial processes is essential for effective peak shaving strategies. This paper proposes a model predictive control (MPC) scheme that can effectively perform peak shaving of the total industrial load. Adopting an MPC-based algorithm design framework enables the development of an effective control strategy for complex systems. The proposed MPC methodology was implemented and tested on the Indian Utility 29 Node Distribution Network (IU29NDN) using the DIgSILENT Power Factory environment. Additionally, the analysis encompasses technical and economic results derived from a simulated storage operation and, taking Puducherry State Electricity Department tariff details, provides significant insights into the application of this method. Full article

In this paper, we propose a battery management algorithm to maximize the lifetime of a parallel-series connected battery pack with heterogeneous states of health in a battery energy storage system. The growth of retired lithium-ion batteries from electric vehicles increases the applications for battery energy storage systems, which typically group multiple individual batteries with heterogeneous states of health in parallel and series to achieve the required voltage and capacity. However, previous work has primarily focused on either parallel or series connections of batteries due to the complexity of managing diverse battery states, such as state of charge and state of health. To address the scheduling in parallel-series connections, we propose a cooperative multi-agent deep Q network framework that leverages multi-agent deep reinforcement learning to observe multiple states within the battery energy storage system and optimize the scheduling of cells and modules in a parallel-series connected battery pack. Our approach not only balances the states of health across the cells and modules but also enhances the overall lifetime of the battery pack. Through simulation, we demonstrate that our algorithm extends the battery pack’s lifetime by up to 16.27% compared to previous work and exhibits robustness in adapting to various power demand conditions. Full article

Aqueous zinc ion batteries (AZIBs) have received a lot of attention in electrochemical energy storage systems for their low cost, environmental compatibility, and good safety. However, cathode materials still face poor material stability and conductivity, which cause poor reversibility and poor rate performance in AZIBs. Herein, a heterogeneous structure combined with cation pre-intercalation strategies was used to prepare a novel CaV₆O₁₆·3H₂O@Ni_0.24V₂O₅·nH₂O material (CaNiVO) for high-performance Zn storage. Excellent energy storage performance was achieved via the wide interlayer conductive network originating from the interlayer-embedded metal ions and heterointerfaces of the two-phase CaNiVO. Furthermore, this unique structure further showed excellent structural stability and led to fast electron/ion transport dynamics. Benefiting from the heterogeneous structure and cation pre-intercalation strategies, the CaNiVO electrodes showed an impressive specific capacity of 334.7 mAh g⁻¹ at 0.1 A g⁻¹ and a rate performance of 110.3 mAh g⁻¹ at 2 A g⁻¹. Therefore, this paper provides a feasible strategy for designing and optimizing cathode materials with superior Zn ion storage performance. Full article

Distributed renewable sources are one of the most promising contributors for DC microgrids to reduce carbon emission and fuel consumption. Although the battery energy storage system (BESS) is widely applied to compensate the power imbalance between distributed generators (DGs) and loads, the impacts of disturbances, DGs, constant power loads (CPLs) and cyber attacks on this system are not simultaneously considered. Based on this, a distributed fuzzy output consensus control strategy is proposed to realize accurate current sharing and operate normally in the presence of denial of service (DoS) attacks and false data injection (FDI) attacks. Firstly, the whole model of the BESS in DC microgrids embedded into disturbance items, DGs, CPLs and resistive loads, is firstly built. This model could be further transformed into standard linear heterogeneous multi-agent systems with disturbance, which lays the foundation for the following control strategy. Then the model of FDI and DoS attacks are built. Meanwhile, the fuzzy logic controller (FLC) is applied to reduce the burden of communication among batteries. Based on these, a distributed output consensus fuzzy control is proposed to realize accurate current sharing among batteries. Moreover, the system under the proposed control in different cases is analyzed. Finally, the feasibility of the proposed control strategy is verified by numerical simulation results and experiment results. Full article

In this paper, we propose a battery management algorithm to optimize the lifetimes of retired lithium batteries with heterogeneous states of health in a battery energy storage system under dynamic power demand. A battery energy storage system allows for the use of retired lithium batteries for applications such as backup power in homes, data centers, etc. In a battery energy storage system, a battery pack consists of several retired batteries connected in parallel or in series to fulfill the required power demand. Owing to the retired batteries’ different capacity levels, i.e., states of health, a scheduling strategy is required to turn battery cells inside the battery pack on and off such that the secondary lifetimes of the retired batteries are extended. To establish the optimal scheduling policy, it is necessary to determine the correct states of each battery cell, including the state of charge and the state of health. To that end, the proposed algorithm first estimates the state of charge and state of health for all cells based on data measured using an extended Kalman filter. Then, a deep reinforcement learning scheduling algorithm is implemented to connect/disconnect the battery cells to/from the battery pack based on their states. Via simulation, we show that the proposed algorithm estimates the state of charge and state of health of each battery cell with low error and extends the lifetime of battery packs by 20.6%, compared to methods proposed in previous works. Full article

The influence of the DC infrastructure on the control of power-storage flow in micro- and smart grids has gained attention recently, particularly in dynamic vehicle-to-grid charging applications. Principal effects include the potential loss of the charge–discharge synchronization and the subsequent impact on the control stabilization, the increased degradation in batteries’ health/life, and resultant power- and energy-efficiency losses. This paper proposes and tests a candidate solution to compensate for the infrastructure effects in a DC microgrid with a varying number of heterogeneous battery storage systems in the context of a multiagent neighbor-to-neighbor control scheme. Specifically, the scheme regulates the balance of the batteries’ load-demand participation, with adaptive compensation for unknown and/or time-varying DC infrastructure influences. Simulation and hardware-in-the-loop studies in realistic conditions demonstrate the improved precision of the charge–discharge synchronization and the enhanced balance of the output voltage under 24 h excessively continuous variations in the load demand. In addition, immediate real-time compensation for the DC infrastructure influence can be attained with no need for initial estimates of key unknown parameters. The results provide both the validation and verification of the proposals under real operational conditions and expectations, including the dynamic switching of the heterogeneous batteries’ connection (plug-and-play) and the variable infrastructure influences of different dynamically switched branches. Key observed metrics include an average reduced convergence time (0.66–13.366%), enhanced output-voltage balance (2.637–3.24%), power-consumption reduction (3.569–4.93%), and power-flow-balance enhancement (2.755–6.468%), which can be achieved for the proposed scheme over a baseline for the experiments in question. Full article

The rapid development and broad deployment of rechargeable batteries have fundamentally transformed modern society by revolutionizing the sectors of consumer electronics, transportation, and grid energy storage. Redox reactions in active battery cathode materials are ubiquitous, complicated, and functionally very important. While a lot of effort has been devoted to investigating redox heterogeneity and its progressive evolution upon prolonged battery cycling, the ultrafast dynamics in these systems are largely unexplored. In this article, we discuss the potential significance of understanding redox dynamics in battery cathodes in the ultrafast time regime. Here, we outline a conceptual experimental design for investigating the ultrafast electron transport in an industry-relevant layered transition metal oxide battery cathode using a plasma-acceleration-based X-ray free-electron laser (FEL) facility. Going beyond the proposed experiment, we provide our perspectives on the use of compact FEL sources for applied research, which, in our view, is an area of tremendous potential. Full article

Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users’ behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users’ behavior in different urban scenarios is predicted through their statistical pattern. A similar approach is used to forecast the EV’s initial SOC. A machine learning approach is adopted to develop a battery-charging behavioral model that takes into account the different EV model charging profiles. The final algorithm combines the different approaches providing a forecasting of the power absorbed by each single charging session and the total power absorbed by charging hubs. The algorithm is applied to different parking scenarios and the results highlight the strong difference in power demand among the different analyzed cases. Full article