Search Results (569)

The introduction of Renewable Energy Sources (RESs) into the electricity grid is changing the price dynamics of the electricity market and creating room for flexibility on the consumption side. This paper investigates different aspects of the interaction between the RES share, electricity spot prices, and electric vehicle (EV) charging strategies. Based on empirical data from Germany, France, and the Czech Republic for the period 2015–2025, four research hypotheses are tested using correlation and regression analysis, cost simulations, and classification algorithms. The results confirm a negative correlation between the RES share and electricity prices, as well as the effectiveness of smart charging in reducing costs. At the same time, it is shown that the occurrence of negative prices is significantly affected by a high RES share. The correlation analysis further suggests that higher production from RESs increases the potential for price optimisation through smart charging. The findings have implications for policymaking aimed at flexible consumption and efficient RES integration. Full article

Flexible energy sources such as electric vehicles and the battery energy storage systems of intelligent distribution systems can provide system-wide auxiliary services such as frequency regulation for power systems. This paper proposes an optimal method for operating the local energy community that is based on two-stage scheduling. Firstly, the basic concepts of the local energy community and flexible service are introduced in detail. Taking LEC as the reserve unit of artificial frequency recovery, an energy information interaction model among LEC, balance service providers, and the power grid is established. Then, a two-stage scheduling framework is proposed to ensure the rationality and economy of community energy scheduling. In the first stage, day-ahead scheduling uses the energy community management center to predict the up/down flexibility capacity that LEC can provide by adjusting the BESS control parameters. In the second stage, real-time scheduling aims at maximizing community profits and scheduling LEC based on the allocation and activation of standby flexibility determined in real time. Finally, the correctness of the two-stage scheduling framework is verified through a case study. The results show that the control parameters used in the day-ahead stage can significantly affect the real-time profitability of LEC, and that LEC benefits more in the case of low BESS utilization than in the case of high BESS utilization and non-participation in frequency recovery reserve. Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. 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

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems. Full article

The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. The proposed framework supports flexible configurations across multiple dimensions, including feature engineering, model selection, and training/testing strategies. It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. The framework’s applicability is demonstrated through five distinct use cases involving SoC estimation and RUL prediction using real-world and benchmark datasets. Experimental results highlight the framework’s adaptability, methodological transparency, and robust predictive performance across various battery chemistries, usage profiles, and degradation conditions. This work contributes to a standardized approach that facilitates the reproducibility, comparability, and practical deployment of ML-based battery analytics. Full article

The low-carbon transition of the global energy system is an urgent necessity to address climate change and meet growing energy demand. As a major source of energy consumption and emissions, buildings play a key role in this transition. This study systematically analyzes the flexible resources of building energy systems and vehicle-to-grid (V2G) interaction technologies, and mainly focuses on the regulation characteristics and coordination mechanisms of distributed energy supply (renewable energy and multi-energy cogeneration), energy storage (electric/thermal/cooling), and flexible loads (air conditioning and electric vehicles) within regional energy systems. The study reveals that distributed renewable energy and multi-energy cogeneration technologies form an integrated architecture through a complementary “output fluctuation mitigation–cascade energy supply” mechanism, enabling the coordinated optimization of building energy efficiency and grid regulation. Electricity and thermal energy storage serve as dual pillars of flexibility along the “fast response–economic storage” dimension. Air conditioning loads and electric vehicles (EVs) complement each other via thermodynamic regulation and Vehicle-to-Everything (V2X) technologies, constructing a dual-dimensional regulation mode in terms of both power and time. Ultimately, a dynamic balance system integrating sources, loads, and storage is established, driven by the spatiotemporal complementarity of multi-energy flows. This paper proposes an innovative framework that optimizes energy consumption and enhances grid stability by coordinating distributed renewable energy, energy storage, and flexible loads across multiple time scales. This approach offers a new perspective for achieving sustainable and flexible building energy systems. In addition, this paper explores the application of demand response policies in building energy systems, analyzing the role of policy incentives and market mechanisms in promoting building energy flexibility. Full article

Due to the absence of engine noise in new energy vehicles, road noise and wind noise become particularly noticeable. Therefore, studying the noise transmission through car doors is essential to effectively reduce the impact of these noises on the passenger compartment. To address the optimization of the sound absorption performance of single-layer porous plates combined with lightweight glass wool used in the doors of electric vehicles, this study established a microscopic acoustic performance analysis model based on the transfer matrix method and sound transmission loss theory. The effects of medium type, perforation rate, perforation radius, material thickness, and porosity on the sound absorption coefficient, impedance characteristics, and reflection coefficient were systematically investigated. Results indicate that in the high-frequency range (above 1200 Hz), the sound absorption coefficients of both rigid and flexible media can reach up to 0.9. When the perforation rate increases from 0.01 to 0.2, the peak sound absorption coefficient in the high-frequency band (1400–2000 Hz) rises from 0.45 to 0.85. Increasing the perforation radius to 0.03 m improves acoustic impedance matching. This research provides theoretical support and a parameter optimization basis for the design of acoustic packaging materials for electric vehicles, contributing significantly to enhancing the interior acoustic environment. Full article

The widespread adoption of electric vehicles introduces significant challenges to power grid stability due to uncoordinated large-scale charging and discharging behaviors. By addressing these challenges, mobile energy storage systems emerge as a flexible resource. To maximize the synergistic potential of jointly scheduling electric vehicles and mobile energy storage systems, this study develops a collaborative scheduling model incorporating the prediction of geographically and chronologically varying distributions of electric vehicles. Non-dominated sorting genetic algorithm-III is then applied to solve this model. Validation through case studies, conducted on the IEEE-69 bus system and an actual urban road network in southern China, demonstrates the model’s efficacy. Case studies reveal that compared to the initial disordered state, the optimized strategy yields a 122.6% increase in profits of the electric vehicle charging station operator, a 44.7% reduction in costs to the electric vehicle user, and a 62.5% decrease in voltage deviation. Furthermore, non-dominated sorting genetic algorithm-III exhibits superior comprehensive performance in multi-objective optimization when benchmarked against two alternative algorithms. Full article

This study addresses the Plug-in Hybrid Electric Vehicle Routing Problem (PHEVRP), an extension of the classical VRP that incorporates energy mode switching and partial charging strategies. We propose a novel routing model that integrates three energy modes—fuel-only, electric-only, and hybrid—along with partial recharging decisions to enhance energy flexibility and reduce operational costs. To overcome the computational challenges of large-scale instances, a Dantzig–Wolfe decomposition algorithm is designed to efficiently reduce the solution space via column generation. Experimental results demonstrate that the hybrid-mode with partial charging strategy consistently outperforms full-charging and single-mode approaches, especially in clustered customer scenarios. To further evaluate algorithmic performance, an Ant Colony Optimization (ACO) heuristic is introduced for comparison. While the full model fails to solve instances with more than 30 customers, the DW algorithm achieves high-quality solutions with optimality gaps typically below 3%. Compared to ACO, DW consistently provides better solution quality and is faster in most cases, though its computation time may vary due to pricing complexity. Full article

The growing integration of renewable energy sources (RESs) into modern power systems calls for enhanced flexibility and control mechanisms. Conventional demand response (DR) strategies, such as price-based and incentive-driven methods, often encounter challenges that limit their effectiveness. This paper proposes a novel DR approach grounded in Customer Directrix Load (CDL) and formulated through Stackelberg game theory. A bilevel optimization framework is established, with air conditioning (AC) systems and electric vehicles (EVs) serving as the main DR participants. The problem is addressed using a genetic algorithm. Simulation studies on a modified IEEE 33-bus distribution system reveal that the proposed strategy significantly improves RES accommodation, reduces power curtailment, and yields mutual benefits for both system operators and end users. The findings highlight the potential of the CDL-based DR mechanism in enhancing operational efficiency and encouraging proactive consumer involvement. Full article

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article

Due to the limitations of current battery technologies—such as lower energy density and high cost compared to fossil fuels—electric vehicles (EVs) face constraints in applications requiring extended range or heavy payloads, such as refuse trucks. As a midterm solution, hybrid electric vehicles (HEVs) combine internal combustion engines (ICEs) and electric powertrains to enable flexible energy usage, particularly in urban duty cycles characterized by frequent stopping and idling. This study introduces a model-based energy management strategy using the Equivalent Consumption Minimization Strategy (ECMS), tailored for a retrofitted series hybrid refuse truck. A conventional ISUZU NPR 10 truck was instrumented to collect real-world driving and operational data, which guided the development of a vehicle-specific ECMS controller. The proposed strategy was evaluated over five driving cycles—including both standardized and measured urban scenarios—under varying load conditions: Tare Mass (TM) and Gross Vehicle Mass (GVM). Compared with a rule-based control approach, ECMS demonstrated up to 14% improvement in driving range and significant reductions in exhaust gas emissions (CO, NO_x, and CO₂). The inclusion of auxiliary load modeling further enhances the realism of the simulation results. These findings validate ECMS as a viable strategy for optimizing fuel economy and reducing emissions in hybrid refuse truck applications. Full article

The rapid growth of electric vehicles (EVs) poses significant challenges to the safe operation of charging stations and distribution networks. Variations in charging power across different EV manufacturers lead to substantial load fluctuations at charging stations. In some tourist cities in China, charging loads can surge at specific times, yet existing research mainly focuses on optimizing station location and basic capacity configuration, neglecting sudden peak load management. To address this, we propose a method that enhances charging station carrying capacity (CSCC) by coordinating multi-flexibility resources. This method optimizes the configuration of soft open points (SOPs) to enable flexible interconnections between feeders and incorporates elastic load scheduling for data centers. An optimization model is developed to coordinate these flexible resources, thereby improving the CSCC. Case studies demonstrate that this approach effectively increases CSCC at lower costs, facilitates the utilization of renewable energy, and enhances the overall system economy. The results validate the feasibility and effectiveness of the proposed approach, offering new insights for urban grid planning and EV charging stations optimization. Full article

This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load Balancer (SLB), an AI-driven intrusion detection system (AIDS), and a Real-Time Analytics Engine (RAE). These parts use advanced machine learning methods like Support Vector Machines (SVMs), autoencoders, and reinforcement learning (RL) to make the system more flexible, secure, and efficient. The framework uses federated learning (FL) to protect data privacy and make decisions in a decentralized way, which lowers the risks that come with centralizing data. The framework makes load distribution 23.5% more efficient, cuts average wait time by 17.8%, and predicts station-level demand with 94.2% accuracy, according to simulation results. The AI-based intrusion detection component has precision, recall, and F1-scores that are all over 97%, which is better than standard methods. The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent. Full article