Search Results (50)

Public transport corporations in Tamil Nadu face increasing operational and financial pressure due to rising diesel fuel prices, maintenance costs, and operational inefficiencies associated with the conventional bus systems. In many districts, diesel-powered public transport services also suffer from irregular vehicle dispatch and poor timetable adherence, leading to unreliable passenger service. Meanwhile, the rapid penetration of electric two-wheelers and four-wheelers indicates a broader transition towards electrified mobility. Extending electrification to public transport requires prudently designed operational planning, as electric buses operate under battery capacity constraints and charging coordination constraints. In such systems, strict adherence to the scheduling of trips and efficient energy management becomes critical for maintaining service reliability. To address these challenges, this study proposes a Tri-Level Hybrid Electric Bus Scheduling (TLH-EBS) framework integrating Particle Swarm Optimization for global search, Rule-Based Scoring Large Neighborhood Search for adaptive schedule improvement, and Mixed Integer Linear Programming for exact repair optimization. The framework simultaneously optimizes fleet size, depot charging infrastructure allocation, and daily bus assignment under timetable constraints. The proposed model has been applied in three interconnected corridors in Madurai District, which are Thirumangalam, Arapalayam, and Mattuthavani, covering 810 scheduled daily timetabled trips between 05:00 AM and 12:30 AM. Computational results show that the hybrid framework has achieved a 2.8% reduction in annual scheduling cost compared to the best conventional optimization method. Furthermore, compared to equivalent diesel-based operations, the optimized electric system has demonstrated approximately 32.3% annual cost savings, confirming the economic viability of integrated fleet–charger scheduling for district-level electric bus deployment. Full article

Electrified railways are major industrial electricity consumers. The Grid-Source-Storage-Vehicle (GSSV) system supports a more sustainable railway power supply by improving local renewable energy utilization, strengthening multi-source energy coordination, and promoting low-carbon development. However, existing rule-based energy management strategies (EMS) remain limited in their ability to support the efficient coordinated operation of the GSSV system. Moreover, under strong source-load fluctuations, conventional optimization-based EMS often fail to provide sufficiently reliable and responsive decision-making for real-time operation of GSSV systems. To address these issues, this paper proposes a real-time EMS based on a rule-guided enhanced non-dominated sorting genetic algorithm (RG-NSGA-II). First, based on the GSSV architecture, the operating modes of the system under different working conditions are systematically analyzed, and a corresponding rule-based EMS is designed. Then, a multi-objective optimization model considering system economic performance and grid power-intake fluctuation is formulated. Furthermore, a coordination mechanism between the rule-based EMS and the optimization EMS is developed. By embedding power commands generated by the rule-based EMS into the optimization EMS and regulating their activation through a time threshold, the proposed method improves the reliability, economic efficiency, and real-time performance of the EMS. Finally, the proposed method is validated, and the results show that the proposed real-time EMS ensures effective utilization of RE, improves power coordination efficiency and operational adaptability under fluctuating operating conditions, and delivers tangible environmental and economic sustainability benefits for electrified railway power supply systems. Full article

To enhance access to efficient and low-carbon public transportation, the city of Alexandria, Egypt, has introduced a fleet of electric buses. Additionally, an ongoing project aims to upgrade and electrify the existing urban railway system, which is expected to alleviate traffic congestion in this densely populated city. The implementation of electric vehicle (EV) parking facilities is also under consideration. This paper investigates the integration of photovoltaic (PV) systems and green hydrogen-powered gas turbines as components of the integrated energy system (IES). An optimal energy management strategy is proposed to maximize the benefits of incorporating renewable energy sources into the urban transportation system (UTS). The proposed energy management algorithm incorporates demand-side management (DSM) for UTS loads and EVs, increasing the complexity of the decision-making process due to the high uncertainty of decision variables. To address this challenge, a modified multi-agent reinforcement learning (MRL) approach is employed, in which uncertainty is incorporated through stochastic environment sampling. Simulation results demonstrate the economic potential of integrating renewable and sustainable energy resources into the IES of the electrified urban transportation system, achieving a 40.2% reduction in the average daily energy consumption cost. Full article

The global clean energy transition requires power conversion technologies that combine high efficiency, operational flexibility, and reduced environmental impact over their entire service life. Solid-state transformers (SSTs) have emerged as a promising alternative to conventional line-frequency transformers, offering bidirectional power flow, high-frequency isolation, and advanced control capabilities that support renewable integration and electrified infrastructures. This paper presents a comparative life cycle assessment (LCA) of conventional transformers and SSTs across representative power-system applications, including residential and industrial distribution networks, electric vehicle fast-charging infrastructure, and transmission–distribution interface substations. The analysis follows a cradle-to-grave approach and is based on literature-derived LCA data, manufacturer specifications, and harmonized engineering assumptions applied consistently across all case studies. The results show that, under identical assumptions, SST-based solutions are associated with indicative lifecycle CO₂ emission reductions of approximately 10–30% compared to conventional transformers, depending on power rating and operating profile (≈90–1000 t CO₂ over 25 years across the four cases). These reductions are primarily driven by lower operational losses and reduced material intensity, while additional system-level benefits arise from enhanced controllability and compatibility with renewable-rich and hybrid AC/DC grids. The study also identifies key challenges that influence the sustainability performance of SSTs, including higher capital cost, thermal management requirements, and the long-term reliability of power-electronic components. Overall, the results indicate that SSTs represent a relevant enabling technology for future low-carbon power systems, while highlighting the importance of transparent assumptions and lifecycle-oriented evaluation when comparing emerging grid technologies. Full article

Copper is a strategic raw material and an important component in electric motors, widely used across industries because of its excellent conductivity and recyclability. It plays an important role in the transformation from fossil fuel-based systems to green, electrified systems. However, substantial material losses continue throughout the lifecycle of electric motors, even with copper’s intrinsic capacity for circularity. Also, copper’s increasing demand, which is driven by the emergence of electric vehicles, industrial electrification, and renewable energy infrastructure, poses questions regarding its sustainable supply. The recovery of secondary copper sources from end-of-life (EoL) products is becoming more and more important in this context. However, it is still difficult to achieve circularity of copper, especially from industrial electric motors. This study investigates the challenges of closing the loop for copper during the lifecycle of motors in industrial applications. Based on an examination of EoL strategies, material flow insights, and practical investigation, the research pinpoints significant inefficiencies in the current processes. The widespread use of scraping as an approach of end-of-life management is one significant issue. Most of the electric motors are not built to separate their components, which makes both mechanical and manual disassembly difficult. The quality of recovered copper is thus compromised by the dominance of mixed metal shredding methods in the recycling step. This study highlights the need for systemic changes in addition to technical solutions to address copper circularity issues. It requires a focus on circularity in designing, giving disassembly and metal recovery a priority. This study focuses on circularity and its technological challenges in a value chain of copper. It not only identifies different processes such as supply chain disconnections and design constraints, but it also suggests workable solutions to close the copper flow loop in the electric motor sector. Copper quality and recovery is ultimately a problem involving design, technology, and cooperation, in addition to resources. This study supports the transition to a more sustainable and circular electric motor industry by offering a basis for directing such changes in industry practices and prospective EU regulations. Full article

The dynamic growth of the electrified vehicle (xEV) market, including both electric and hybrid vehicles, has increased the demand for advanced Battery Management Systems (BMS). From an energy-systems perspective, xEV batteries act as distributed energy storage units that strongly interact with power grids, renewable generation, and charging infrastructure, making their efficient control a key element of low-carbon energy systems. Traditional BMS methods face challenges in accurately estimating key battery states and parameters, especially under dynamic operating conditions. This review systematically analyzes the progress in applying artificial intelligence, machine learning, and other advanced computational and data-driven algorithms to improve the performance of xEV battery management with a particular focus on energy efficiency, safe utilization of stored electrochemical energy, and the interaction between vehicles and the power system. The literature analysis covers key research trends from 2020 to 2025. This review covers a wide range of applications, including State of Charge (SOC) estimation, State of Health (SOH) prediction, and thermal management. We examine the use of various methods, such as deep learning, neural networks, genetic algorithms, regression, and also filtering algorithms, to solve these complex problems. This review also classifies the research by geographical distribution and document types, providing insight into the global landscape of this rapidly evolving field. By explicitly linking BMS functions with energy-system indicators such as charging load profiles, peak-load reduction, self-consumption of photovoltaic generation, and lifetime-aware energy use, this synthesis of contemporary research serves as a valuable resource for scientists and engineers who wish to understand the latest achievements and future directions in data-driven battery management and its role in modern energy systems. Full article

Charging electric vehicles (EVs) and integrating renewable energy sources (RESs) are becoming key aspects of residential energy systems. However, the variability of RES generation, combined with uncontrolled EV charging, poses challenges for reliability, power quality, and supply-demand balancing within communities. The challenges only grow when considering other electrified building loads as well. Accurate forecasting of power demand and renewable generation is essential for efficient and sustainable grid operation, optimal use of RESs, and effective energy trading within communities. Deep learning (DL), including supervised, unsupervised, and reinforcement learning (RL), has emerged as a promising solution for predicting consumer demand, renewable generation, and managing energy flows in residential environments. This paper provides a comprehensive review of the development and application of these methods for forecasting and energy management in residential communities. Evaluation metrics across studies indicate that supervised learning can achieve highly accurate forecasting results, especially when integrated with unsupervised K-means clustering and data decomposition. These methods help uncover patterns and relationships within the data while reducing noise, thereby enhancing prediction accuracy. RL shows significant potential in control applications, particularly for charging strategies. Similarly to how V2G-simulators model individual EV usage and simulate large fleets to generate grid-scale predictions, RL can be applied to various aspects of EV fleet management, including vehicle dispatching, smart scheduling, and charging coordination. Traditional methods are also used across different applications and help utilities with planning. However, these methods have limitations and may not always be completely accurate. Our review suggests that integrating hybrid supervised-unsupervised learning methods with RL can significantly improve the sustainability and resilience of energy systems. This approach can improve demand and generation forecasting while enabling smart charging coordination and scheduling for scalable EV fleets integrated with building electrification measures. Furthermore, the review introduces a unifying conceptual framework that links forecasting, optimization, and policy coupling through hierarchical deep learning layers, enabling scalable coordination of EV charging, renewable generation, and building energy management. Despite methodological advances, real-world deployment of hybrid and deep learning frameworks remains constrained by data-privacy restrictions, interoperability issues, and computational demands, highlighting the need for explainable, privacy-preserving, and standardized modeling approaches. To be effective in practice, these methods require robust data acquisition, optimized forecasting and control models, and integrated consideration of transport, building, and grid domains. Furthermore, deployment must account for data privacy regulations, cybersecurity safeguards, model interpretability, and economic feasibility to ensure resilient, scalable, and socially acceptable solutions. Full article

Hydrogen-powered hybrid trains equipped with fuel cells (FC) and batteries represent a promising alternative to diesel traction on non-electrified railway lines and have significant potential to support modal shifts toward more sustainable transport systems. This study presents the development of a flexible MATLAB-based tool for the dynamic simulation of fuel cell–battery hybrid powertrains. The model integrates train dynamics, rule-based energy management, system efficiencies, and component degradation, enabling both energy and cost analyses over the vehicle’s lifetime. The objective is to assess the techno-economic performance of different powertrain configurations. Sensitivity analyses were carried out by varying two sizing parameters: the nominal power of the fuel cell (parameter m) and the total battery capacity (parameter n), across multiple real-world railway routes. Results show a slight reduction in lifecycle costs as m increases (5.1 €/km for m = 0.50) mainly due to a lower FC degradation. Conversely, increasing battery capacity (n) lowers costs by reducing cycling stress for both battery and FC, from 5.3 €/km (n = 0.10) to 4.5 €/km (n = 0.20). In general, lowest values of m and n provide unviable solutions as the battery discharges completely before the end of the journey. The study highlights the critical impact of the operational profile: for a fixed powertrain configuration (m = 0.45, n = 0.20), the specific cost dramatically increases from 4.44 €/km on a long, flat route to 15.8 €/km on a hilly line and up to 76.7 €/km on a mountainous route, primarily due to severe fuel cell degradation under transient loads. These findings demonstrate that an “all-purpose” train sizing approach is inadequate, confirming the necessity of route-specific powertrain optimization to balance techno-economic performance. Full article

Despite the ongoing electromobility revolution in urban areas, fleet managers still prefer combustion engines over electric vehicles. Fleet electrification can deliver tangible benefits not only for the urban environment but also for the company itself. However, this requires a robust economic and technical analysis approach. This study assesses the technical and economic viability of integrating electric vehicle (EV) fleets into the Turkish manual frequency recovery reserve (mFRR) market. Using a life-cycle costing (LCC) framework, three operational scenarios are modeled: Baseline (leased EVs without V2G), V2G+ (leased EVs with aggregator-based mFRR), and High Utilization (owned EVs with full V2G integration and increased rental activity). The baseline scenario assumes a net cost of USD 142,500 over 10 years, excluding revenue share. V2G+ reduces this amount to USD 137,000, generating an annual income of approximately USD 4400 from its share of the frequency reserve. A high utilization scenario, combining V2G with ownership and higher rental income, reduces the net LCC to USD 125,500 and generates over USD 12,000 annually, reaching breakeven around year 7. Sensitivity analyses show that the financial profitability of the system is significantly influenced by EV purchase prices, aggregator fees, mFRR capacity payments, and vehicle utilization rates. Adding a 30–50% solar-powered charging enclosure further reduces operating costs by up to USD 21,500, demonstrating the synergistic potential of integrating V2G and distributed photovoltaics. These results influence not only the priorities for electrifying the urban vehicle fleet, but also smart city regulations in the area of energy management, through the development of bidirectional charging standards and pilot implementation of V2G in emerging markets such as Turkey. Full article

Electrified powertrains in the transportation sector have increased significantly in recent years, thanks to the need for decarbonization of the on-the-road transport means. However, management of powertrains still deserves particular attention to assess necessary improvements for reducing electric consumption and increasing the mileage of the vehicles. In this regard, electric motor cooling is essential for maintaining optimal performance and longevity. In fact, as electric motors operate, they generate heat due to electric and magnetic phenomena as well as mechanical friction. If not properly managed, this heat can lead to decreased efficiency, accelerated wear, or even failure of critical components. Effective cooling systems ensure that the motor runs within its ideal temperature range, reducing the occurrence of the mentioned concerns. This improves operational reliability and, at the same time, contributes to energy savings and reduced maintenance costs over the components’ life. In this study, the cooling of the rotor of a 130-kW electric motor via refrigerating fluid circulating inside the shaft has been investigated. Two configurations of fluid passages have been considered: a direct-through flow crossing the shaft along its axis and a hollow shaft with recirculating flow, with three types of rotating helical configurations at different pitches. The benefits when using nanofluids as a cooling medium have also been evaluated to enhance the heat transfer coefficient and decrease temperature values. Compared with the baseline configuration using standard fluids (water), the proposed solution employing nanofluids demonstrates effectiveness in terms of heat transfer coefficients (up to 28% higher than pure water), with limited impact on pressure losses, thus reducing rotor temperature by up to 30 K with respect to the baseline. This study opens the possibility of integrating the cooling of the rotor with whole electric motor cooling for electric and hybrid powertrains. Full article

With major effects on power grids and people’s lifestyles, the quick uptake of electric vehicles (EVs) poses serious problems for the robustness of charging infrastructure. By enabling spatiotemporally optimal charging strategies that optimize grid operations, big data technologies provide game-changing solutions. In order to solve the following issues, this paper summarizes state-of-the-art applications of EV charging big data, which are derived from vehicles, charging stations, and power grids: (1) optimized control of grid operation; (2) charging infrastructure layout; (3) battery development; and (4) safety of charging equipment. Future research opportunities include: (1) deep integration of intelligent transportation and smart grids; (2) renewable energy and intelligent energy management optimization; (3) synergizing smart homes with EVs; and (4) AI for energy demand forecasting and automated management. This study establishes big data as a pivotal tool for low-carbon EV transition, providing actionable frameworks for researchers and policymakers to harmonize electrified transport with energy sustainability goals. Full article

The increasing penetration of electrified vehicles is accelerating the evolution of on-board and off-board charging systems, which must deliver higher efficiency, power density, safety, and bidirectionality under increasingly demanding constraints. This article presents a system-level review of state-of-the-art charging architectures, with a focus on galvanically isolated power conversion stages, wide-bandgap-based switching devices, battery pack design, and real-world implementation trends. The analysis spans the full energy path—from grid interface to battery terminals—highlighting key aspects such as AC/DC front-end topologies (Boost, Totem-Pole, Vienna, T-Type), high-frequency isolated DC/DC converters (LLC, PSFB, DAB), transformer modeling and optimization, and the functional integration of the Battery Management System (BMS). Attention is also given to electrochemical cell characteristics, pack architecture, and their impact on OBC design constraints, including voltage range, ripple sensitivity, and control bandwidth. Commercial solutions are examined across Tier 1–3 suppliers, illustrating how technical enablers such as SiC/GaN semiconductors, planar magnetics, and high-resolution BMS coordination are shaping production-grade OBCs. A system perspective is maintained throughout, emphasizing co-design approaches across hardware, firmware, and vehicle-level integration. The review concludes with a discussion of emerging trends in multi-functional power stages, V2G-enabled interfaces, predictive control, and platform-level convergence, positioning the on-board charger as a key node in the energy and information architecture of future electric vehicles. Full article

With the development of dynamic wireless power transfer (DWPT) technology, the introduction of wireless charging lanes (WCLs) in traffic systems is seen as a promising trend for electrified transportation. Though there has been extensive discussion about the planning and allocation of WCLs in different situations, studies on traffic control models for WCLs are relatively lacking. Thus, this paper aims to design a coordinated optimization strategy for managing electric vehicle (EV) traffic on an expressway network, which integrates a corridor traffic flow model with a wireless power transmission model. Two components are considered in the control objective: the total energy increased for the EVs and the total number of EVs served by the expressway, over the problem horizon. By setting the trade-off coefficients for these two objectives, our model can be used to achieve mixed optimization of WCL traffic management. The decisions include metering of different on-ramps as well as routing plans for different groups of EVs defined by origin/destination pairs and initial SOC levels. The control problem is formulated as a novel linear programming model, rendering an efficient solution. Numerical examples are used to verify the effectiveness of the proposed traffic control model. The results show that with the properly designed traffic management strategy, a notable increase in charging performance can be achieved by compromising slightly the traffic performance while maintaining overall smooth operation throughout the expressway system. Full article

The integration of Electric Vehicles (EVs) with solar power generation is important for decarbonizing the economy. While electrifying transportation reduces Greenhouse Gas (GHG) emissions, its success depends on ensuring that EVs are charged with clean energy, requiring significant increases in photovoltaic capacity and robust Demand-Side Management (DSM) solutions. EV charging patterns, such as home, workplace, and public charging, need adapted strategies to match solar generation. This study analyzes a system designed to meet a unitary hourly average energy demand (8760 MWh annually) using an optimization framework that balances PV capacity and battery storage to ensure reliable energy supply. Historical solar data from 22 years is used to analyze seasonal and interannual fluctuations. The results show that solar PV alone can cover around 30% of the demand without DSM, rising to nearly 50% with aggressive DSM measures, using PV capacities of 1.0–2.0 MW. The optimization reveals that incorporating battery storage can achieve near 100% coverage with PV power of 8.0–9.0 MW. Moreover, DSM reduces required storage from 18 to about 10 MWh. These findings highlight the importance of integrating optimization-based energy management strategies to enhance system efficiency and cost-effectiveness, offering a pathway toward a more sustainable and resilient EV charging infrastructure. Full article

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle’s hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO₂ emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO₂ emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO₂/km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency. Full article