Search Results (134)

This paper presents a comparative study of data-driven modeling approaches for vanadium redox flow batteries (VRFBs), utilizing Multiple Linear Regression (MLR) and Random Forest (RF) algorithms. Experimental voltage–capacity datasets from a 1 kW/1 kWh VRFB system were digitized, processed, and used for model training, validation, and testing. The MLR model, built using eight optimized features, achieved a mean error (ME) of 0.0204 V, a residual sum of squares (RSS) of 8.87, and a root mean squared error (RMSE) of 0.1796 V on the test data, demonstrating high predictive performance in stationary operating regions. However, it exhibited limited accuracy during dynamic transitions. Optimized through out-of-bag (OOB) error minimization, the Random Forest model achieved a training RMSE of 0.093 V and a test RMSE of 0.110 V, significantly outperforming MLR in capturing dynamic behavior while maintaining comparable performance in steady-state regions. The accuracy remained high even at lower current densities. Feature importance analysis and partial dependence plots (PDPs) confirmed the dominance of current-related features and SOC dynamics in influencing VRFB terminal voltage. Overall, the Random Forest model offers superior accuracy and robustness, making it highly suitable for real-time VRFB system monitoring, control, and digital twin integration. This study highlights the potential of combining machine learning algorithms with electrochemical domain knowledge to enhance battery system modeling for future energy storage applications. Full article

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

The energy transition toward greater electrification leads to incentives in public transportation fed by catenary-powered networks. In this context, emerging technological devices such as in-motion-charging vehicles and electric vehicle charging points are expected to be operated while connected to trolleybus networks as part of new electrification projects, resulting in a significant demand for power. To enable a significant increase in electric transportation without compromising technical compliance for voltage and current at grid systems, the implementation of stationary battery energy storage systems (BESSs) can be essential for new electrification projects. A key challenge for BESSs is the selection of the optimal converter topology for charging their batteries. Ideally, the chosen converter should offer the highest efficiency while minimizing size, weight, and cost. In this context, a modular dual-active-bridge converter, considering its operation as a full-power converter (FPC) and a partial-power converter (PPC) with module-shedding control, is analyzed in terms of operation efficiencies and thermal behavior. The goal is to clarify the advantages, disadvantages, challenges, and trade-offs of both power-processing techniques following future trends in the electric transportation sector. The results indicate that the PPC achieves an efficiency of 98.58% at the full load of 100 kW, which is 1.19% higher than that of FPC. Additionally, higher power density and cost effectiveness are confirmed for the PPC. Full article

Electric passenger vehicles are set to dominate the European car market, driven by EU climate policies and the 2035 ban on internal combustion engine production. This study assesses the sustainability of this transition, focusing on global warming potential and Critical Raw Material (CRM) extraction throughout its life cycle. The intensive use of CRMs raises environmental, economic, social, and geopolitical concerns. These materials are scarce and are concentrated in a few politically sensitive regions, leaving the EU highly dependent on external suppliers. The extraction, transport, and refining of CRMs and battery production are high-emission processes that contribute to climate change and pose risks to ecosystems and human health. A Life Cycle Assessment (LCA) was conducted, using OpenLCA software and the Ecoinvent 3.10 database, comparing a Peugeot 308 in its diesel and electric versions. This study adopts a cradle-to-grave approach, analyzing three phases: production, utilization, and end-of-life treatment. Key indicators included Global Warming Potential (GWP100) and Abiotic Resource Depletion Potential (ADP) to assess CO₂ emissions and mineral resource consumption. Technological advancements could mitigate mineral depletion concerns. Li-ion battery recycling is still underdeveloped, but has high recovery potential, with the sector expected to expand significantly. Moreover, repurposing used Li-ion batteries for stationary energy storage in renewable energy systems can extend their lifespan by over a decade, decreasing the demand for new batteries. Such innovations underscore the potential for a more sustainable electric vehicle industry. Full article

To alleviate the impact of economic and environmental detriments caused by the increased demands of electric vehicle battery production and disposal, the use of spent batteries in second-life stationary applications such as energy storage for renewable sources or backup power systems, offers many benefits. This paper focuses on reducing the energy consumption cost and greenhouse gas emissions of Internet-of-Things-enabled campus microgrids by installing solar photovoltaic panels on rooftops alongside energy storage systems that leverage second-life batteries, a gas-fired campus power plant, and a wind turbine while considering the potential loads of a prosumer microgrid. A linear optimization problem is derived from the system by scheduling energy exchanges with the Ontario grid through net metering and solved by using Python 3.11. The aim of this work is to support Sustainable Development Goals, namely 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action). A comparison between a base case scenario and the results achieved with the proposed scenarios shows a significant reduction in electricity cost and greenhouse gas emissions and an increase in self-consumption rate and renewable fraction. This research work provides valuable insights and guidelines to policymakers. Full article

The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging. Full article

This paper explores cleaner and techno-economically viable solutions to provide electricity, heat, and cooling using green hydrogen (H₂) and green ammonia (NH₃) across the entire decarbonized value chain. We propose integrating a 100% hydrogen-fueled internal combustion engine (e.g., Jenbacher JMS 420) as a stationary backup solution and comparing its performance with other backup technologies. While electrochemical storage systems, or battery energy storage systems (BESSs), offer fast and reliable short-term energy buffering, they lack flexibility in relocation and typically involve higher costs for extended backup durations. Through five case studies, we highlight that renewable-based energy supply requires additional capacity to bridge longer periods of undersupply. Our results indicate that, for cost reasons, battery–electric solutions alone are not economically feasible for long-term backup. Instead, a more effective system combines both battery and hydrogen storage, where batteries address daily fluctuations and hydrogen engines handle seasonal surpluses. Despite lower overall efficiency, gas engines offer favorable investment and operating costs in backup applications with low annual operating hours. Furthermore, the inherent fuel flexibility of combustion engines eventually will allow green ammonia-based backup systems, particularly as advancements in small-scale thermal cracking become commercially available. Future studies will address CO₂ credit recognition, carbon taxes, and regulatory constraints in developing more effective dispatch and master-planning solutions. Full article

This article presents a comprehensive analysis of the potential for microgeneration of electrical energy from human physical activity and reviews current commercial and research solutions, including stationary bicycles, treadmills, rowing ergometers, strength equipment, and kinetic floor systems. The physiological foundations of human energy generation are examined, with attention to key factors such as age, gender, fitness level, maximum oxygen uptake, heart rate, and hydration. The study includes mathematical models of energy conversion from metabolic to electrical output, incorporating fatigue as a limiting factor in long-duration performance. Available energy storage technologies (e.g., lithium-ion batteries, supercapacitors, and flywheels) and intelligent energy management systems (EMS) for use in sports facilities and net-zero energy buildings are also reviewed. As part of the study, a conceptual design of a multifunctional training and diagnostic device is proposed to illustrate potential technological directions. This device integrates microgeneration with dynamic physiological monitoring and adaptive load control through power electronic conversion. The paper highlights both the opportunities and limitations of harvesting human-generated energy and outlines future directions for sustainable energy applications in fitness environments. A preliminary economic analysis is also included, showing that while the energy payback alone is limited, the device offers commercial potential when combined with diagnostic and smart fitness services and may contribute to broader building energy efficiency strategies through integration with intelligent energy systems. Full article

This paper proposes the sizing optimization method and energy management strategy for a stationary hybrid energy storage system dedicated to a DC traction power supply system. The hybrid energy storage system consists of two modules—a supercapacitor, mainly dedicated to regenerative energy utilization, and a Li-ion battery, aimed to peak power reduction. The sizing method and energy management strategy proposed in this paper aim to reduce the aging effect of lithium-ion batteries. It is shown that the parameters of both modules could be sized independently. The supercapacitor module parameters are sized based on the results of a simulation determining the regenerative power, resulting in limited catenary receptivity. The simulation model of the DC electrification system is validated by comparing the results of the simulation with the measurements of 15 min average power in a 24 h cycle as average values of one year. The battery module is sized based on the statistical data of 15 min substation power value occurrences. The battery energy capacity, its maximum discharge C-rate, and the conditions determining its operation are optimized to achieve the maximum ratio of annual income resulting from peak power reduction to annual operating cost resulting from the battery aging process and total life cycle. The case study prepared for a typical 3 kV DC substation with mixed railway traffic shows that peak power could be reduced by ~1 MW, giving a ~10-year payback period for battery module installation, while the energy consumption could be decreased by 1.9 MWh/24 h, giving a ~7.5-year payback period for supercapacitor module installation. The payback period of the whole energy storage system (ESS) is ~8.4 years. Full article

The occurrence of extreme disasters, such as seismic hazards, can significantly disrupt transportation and distribution networks (DNs), consequently impacting the post-disaster recovery process. Restoring load using distributed generation represents an important approach to improving the resilience of DNs. However, using these resources to provide resilience is not enough to justify having them installed economically. Therefore, this paper proposes a two-stage stochastic mixed-integer programming (SMIP) model for the configuration of stationary energy storage systems (SESSs) and mobile energy storage systems (MESSs) during earthquakes. The proposed model comprehensively considers both normal and disaster operation scenarios of DNs, maximizing the grid’s economic efficiency and security. The first stage is to make decisions about the location and size of energy storage, using a hybrid configuration scheme of second-life batteries (SLBs) for SESSs and fresh batteries for MESSs. In the second stage, the operating costs of DNs are evaluated by minimizing normal operating costs and reducing load loss during seismic events. Additionally, this paper proposes a scenario reduction method based on hierarchical sampling and distance reduction to generate representative fault scenarios under varying earthquake magnitudes. Finally, the progressive hedging algorithm (PHA) is employed to solve the model. The case studies of the IEEE 33-bus and 12-node transportation network are conducted to validate the effectiveness of the proposed method. Full article

Lithium-ion batteries that use lithium iron phosphate (LiFePO₄) as the cathode material and carbon (graphite or MCMB) as the anode have gained significant attention due to their cost-effectiveness, low environmental impact, and strong safety profile. These advantages make them suitable for a wide range of applications including electric vehicles, stationary energy storage, and backup power systems. However, their adoption is hindered by a critical challenge: capacity degradation at elevated temperatures. This review systematically summarizes the corresponding modification strategies including surface modification of the anode and cathode as well as modification of the electrolyte, separator, binder, and collector. We further discuss the control of the charge state, early warning prevention, control of thermal runaway, and the rational application of ML and DFT to enhance the LFP/C high temperature cycling stability. Finally, in light of the current research challenges, promising research directions are presented, aiming at enhancing their performance and stability in such harsh thermal environments. Full article

Batteries are essential for the functionality of electric vehicles (EVs), leading to their design with enhanced performance and durability. Consequently, traction batteries are often replaced while they still retain the properties for use in less stressful demanding applications, with lower power and storage requirements. This serves as a notable opportunity for circular economy. The energy management system presented is designed with lithium-ion batteries coming from EVs and repurposed for electricity storage as a smart backup solution for buildings. The system buys and stores energy from the grid during low-cost periods and utilizes the stored electricity to feed the demand, avoiding high electricity prices and smoothing out peak consumptions exceeding a predefined power limit. To illustrate the proposal, a case study is presented based on the Spanish market, analyzing the impact on the electricity savings for end consumers as well as the extended second-life estimation for a pack of batteries. The analysis of the results will help assess if the system is both economically feasible and environmentally sustainable from a circular economy point of view. Full article

This study explores the potential for PV solar power and battery storage to reduce energy costs in a typical Malian single-family household, highlighting significant cost savings and improved energy reliability. The high solar irradiance throughout the year makes solar power viable for household energy needs. However, most electricity is consumed at night due to air conditioning, with an annual consumption of 12,504 kWh. Cost models for solar power plants and battery energy storage systems, including installation, were developed. Cost parameters were reviewed using the latest literature, distinguishing between current and future cost trends, referred to as Case I and Case II, respectively. Additionally, a feed-in tariff of $0.00 and $0.04 per injected kWh of electricity into the AC mains was considered. The annual return in USD and the return on investment were considered as economic parameters. A small solar power plant with a peak power of up to 3 kW can achieve a high ROI between 70% and 100%. Due to reduced future cost prospects, this ROI could increase to 90% to 130%. However, such a plant can only reach a maximum self-sufficiency of about 40%, as most of the electricity is consumed during nights. A 4 kW power plant can achieve a self-sufficiency of about one-third for an ROI of 57% to 82%, costing approximately $1330 to $1760. When using battery energy storage, a self-sufficiency of 95% has been targeted. With battery storage, the maximum ROI varies from 22.5% to 32.0% with an investment cost of about $9590 to $13,139. Full article

Lithium-ion batteries have been increasingly demonstrated in reuse applications for environmental and economic reasons, and stationary energy storage systems (ESS) and mobile ESS are emerging as reuse applications for electric vehicle batteries. Most mobile ESS deployments are at large scales, necessitating experimental data on thermal runaway (TR) to ensure comprehensive safety. In this study, TR induction and suppression experiments were conducted using fully charged NCM-based batteries at the cell (750 Wh), module (7.5 kWh), and pack (74 kWh) levels. The stepwise TR experiments measured changes in temperature, voltage, heat release rate, volatile organic compound concentrations, and vent gas composition. The suppression experiments assessed the effective water injection rate, timing, and volume required to mitigate TR propagation. The results demonstrate that in the case of TR caused by thermal abuse, early detection of battery abnormalities is possible through monitoring pre-TR indicators, such as temperature and vent gas concentration. It was also confirmed that CO₂ injections can effectively cool the battery without causing damage. Furthermore, it is proposed that rapid water injection, directly contacting the battery immediately after the onset of TR, can successfully prevent TR propagation. Full article

Lithium-ion batteries play a vital role in modern energy storage systems, being widely utilized in devices such as mobile phones, electric vehicles, and stationary energy units. One of the critical challenges with their use is the thermal runaway (TR), typically characterized by a sharp increase in internal pressure. A thorough understanding and accurate prediction of this behavior are crucial for improving the safety and reliability of these batteries. To achieve this, two new combined models were developed: one to simulate the thermal runaway and another to simulate the internal cell pressure. The thermal model tracks a chain of decomposition reactions that eventually lead to TR. At the same time, the pressure model simulates the proportional increase in pressure due to the evaporation of the electrolyte and the gases produced from the decomposition reactions. What sets this work apart is the validation of the pressure model through experimental data, specifically for prismatic lithium-ion cells using NMC chemistries with varying stoichiometries—NMC111 and NMC811. While the majority of the literature focuses on the simulation of temperature and pressure for cylindrical cells, studies addressing these aspects in prismatic cells are much less common. This article addresses this gap by conducting pressure validation experiments, which are hardly documented in the existing studies. Furthermore, the model’s accuracy and flexibility are tested through two experiments, conducted under diverse conditions to ensure robust and adaptive predictions of cell behavior during failure scenarios. Full article