Charging Mode Research Articles

Fiber Bragg Grating-Based Tunable Wavelength Orbital Angular Momentum Beam Generation with

This paper introduces and theoretically analyze the generation of orbital angular momentum (OAM) modes with different polarization in a few-mode optical fiber using short-period gratings (Bragg gratings) at telecommunication wavelengths. The modal characteristics of the supported modes have been studied using a full-vector true-mode analysis. Additionally, a appropriately designed raised-cosine apodized grating is employed to couple the HE11 core mode to the counter-propagating cladding mode. In contrast to the long-period grating based OAM mode generation, the present scheme is free from periodic back-coupling of optical power into the fundamental core mode which drastically enhances the OAM mode purity. The ±1-order OAM beam has been generated by combining the first higher order core modes with a π/2 phase shift between them. This paper also shows that by combining different higher order modes, it is possible to obtain a linearly polarised OAM mode or a circularly polarised OAM mode. Finally, I also report that in fibers with larger core radius, the higher order OAM mode of topological charge ±2 or higher can also be generated using similar approach of using short-period gratings. The topological charge of the OAM mode is verified by observing both the coaxial and off axial interference fringes.

Novel variable charging pricing strategy applied to the multi-objective planning of integrated fast and slow electric vehicle charging stations and distributed energy resources

Electric vehicle charging stations are increasingly being deployed to accommodate the rising demand for electric vehicles. However, this additional load on the distribution system can lead to significant challenges, including increased power losses and undervolage issues. Therefore, this paper proposes the optimized planning of electric vehicle charging stations, along with the integration of distributed energy resources considering the power system operator’s perspective. The approach focuses on minimizing the costs associated with public charging stations and distributed generators, as well as reducing power losses and voltage deviation of the system. Additionally, this study incorporates both fast and slow chargers to address user requirements, while promoting the use of slow charging to help reduce the peak load demand caused by the electric vehicle charging stations. A novel variable pricing strategy for slow charging is also proposed to decrease the operator’s expenses while promoting this mode of charging. The proposed methodology is validated through the integration of a 33-bus distribution test system with a 25-node traffic system. The analysis was conducted using three multi-objective optimization methods: Multi-Objective Cuckoo Search, Multi-Objective Flower Pollination Algorithm, and Non-dominated Sorting Genetic Algorithm II. From the results, it is possible to conclude that the proposed approach provides benefits not only to the system operator but also to EV users, given the reduction in their travel costs. Despite the increased charging demand, the optimization results show a reduction in voltage deviation by 11.57%–16.94% and power losses by 19.71%–24.20% compared to the original system.

A distributed double-layer control algorithm for medium voltage regulation and state of charge consensus of autonomous battery energy storage systems in distribution networks

The increasing integration of Distributed Generation (DG) based on Renewable Energy Sources (RES) in traditional distribution systems necessitates the adoption of smart grid strategies. These strategies rely heavily on energy storage actuation to manage the inherent variability of RES and ensure autonomous operation. This article presents a hierarchical digital control strategy for managing distribution power systems, utilizing Battery Energy Storage Systems (BESS) to regulate voltage amplitude and enhance overall behavior for efficient energy management. At the primary control level, operating at a minute-scale actuation rate, an integral voltage regulation loop determines the BESS power injection or consumption, refined by a fuzzy conditioning of signals that accounts for the state of charge and operational modes of each device. Laplacian and random switching secondary consensus control strategies, operating at a slower rate, ensure coordinated action among individual units. The convergence of these strategies is analytically validated using Lyapunov’s theory under idealized conditions. Extensive dynamic simulations over a 24-hour period demonstrate the proposed strategy’s effectiveness in improving power quality and coordinating voltage regulation, particularly in terms of enhancing the power factor and reducing the standard deviation of voltage and power variables over time in a coordinated manner.

A Novel Parameter Estimation Scheme for Supercapacitors

As reliance on sustainable energy grows, the demand for efficient, high-performance energy storage systems becomes increasingly critical, especially in uninterruptible power supplies (UPS), where reliability and fast transitions are essential. Supercapacitors, with their high power density and rapid charging capabilities, are emerging as strong alternatives to lithium-ion batteries in UPS systems. This paper presents a novel real-time estimation technique for monitoring supercapacitor parameters within a UPS, focusing on the dynamic behavior of these parameters and their evolution over the system’s lifecycle. The proposed estimator demonstrates exceptional accuracy, achieving less than 1% error within 120 ms of startup and nearly zero error thereafter. The estimator’s performance remains robust even as supercapacitor parameters change due to aging effects over the lifespan. The UPS system features a modular design, enabling scalability to accommodate higher power requirements or longer backup durations and adaptability to various supercapacitor types. Experimental results highlight the system’s robustness in both charging and backup modes, emphasizing the potential of supercapacitors as key components in future UPS systems.

Two-stage stochastic robust optimization for capacity allocation and operation of integrated energy systems

To address the challenges posed by various uncertainties in integrated energy systems (IES) for planning and operation, this paper considers the capacity configuration of IES equipment with energy storage systems under a stepped carbon trading mechanism, as well as the planning of electric vehicle (EV) charging stations under different charging modes. A two-stage stochastic robust planning method is proposed, taking into account both short- and long-term uncertainties in renewable energy generation and electric, thermal, and cooling loads. In the first stage, planning decisions are made with the objective of minimizing investment costs, while accounting for the seasonal characteristics of the “source-load” relationship in the planning phase. Stochastic programming is employed to handle long-term uncertainties. In the second stage, operational simulation is performed with the goal of minimizing energy dispatch costs and carbon trading costs, and short-term uncertainties in “source-load” operations are described using robust optimization. The nested column-and-constraint generation (NC&CG) algorithm is used to solve this two-stage model. Finally, the proposed model is applied to an IES in northern China. The results show that orderly EV charging within the IES can reduce both investment and operational costs, as well as carbon emissions. The consideration of a stepped carbon trading mechanism can reduce system carbon emissions and enhance environmental sustainability. IES planning with multiple energy storage types is more economical than with a single energy storage type, and the proposed stochastic robust planning method, which considers both long- and short-term uncertainties, demonstrates stronger reliability and economic performance under extreme conditions.

The Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions

This paper introduces the Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions (HEVRP-NL). This problem involves routing a heterogeneous fleet of electric vehicles, utilizing multiple charging modes, and accounting for time-dependent waiting time functions at charging stations. The problem is modeled using a path-based mixed-integer linear programming formulation. To solve this problem, we present an algorithmic framework that alternates between two components. The first component is an iterated local search algorithm with a problem-specific route evaluation function, which obtains local optimal solutions and generates a pool of high-quality routes. The second component is a set-partitioning model that combines a subset of routes from the pool, which is constructed based on reduced costs, into a feasible solution. We design HEVRP-NL benchmark instances based on the publicly available electric fleet size and mix vehicle routing problem instances, which are used to evaluate our methods. For small-scale HEVRP-NL instances, the proposed model can be employed in a general-purpose mixed integer programming solver to achieve optimal solutions or find good upper bounds. This exact approach serves as an evaluation of our heuristic algorithm’s ability to attain optimal solutions rapidly. Extensive computational results on large-scale HEVRP-NL instances illustrate the advantages of considering non-linear charging functions and show the impact of waiting time at the charging stations. Finally, we conduct experiments on 120 benchmark instances for the E-VRP-NL and 168 benchmark instances for the E-FSMFTW-PR, which are the special cases of our problem. The results indicate that our algorithm outperforms existing approaches from the literature and identifies 32 new best solutions for the E-VRP-NL and 33 new best solutions for the E-FSMFTW-PR, respectively.

Sustainable desalination through hybrid photovoltaic/thermal membrane distillation: Development of an off-grid prototype

Global freshwater scarcity is a critical challenge, particularly severe in remote Australian communities where seawater intrusion and aquifer salinisation exacerbate the need for alternative water resources. Conventional desalination technologies are energy-intensive and unsuitable for rural areas. This study introduces a novel, off-grid, sustainable desalination solution utilising solar-integrated membrane distillation (MD) technology. An innovative, stand-alone prototype combining a hybrid photovoltaic/thermal (PV/T) system with direct contact MD (DCMD) has been designed and developed. This fully integrated PV/T collector efficiently supplies the thermal and electrical energy required for the MD process. The photovoltaic panel generates the necessary electrical energy, while the heat from the PV panel warms the MD system’s feed solution, enhancing overall efficiency through the solar cell cooling effect. In addition, an innovative fan-cooled radiator equipped with two DC fans with a low power consumption rating was designed and customized to be applied as MD system cooling medium within the outdoor setting. The concept development and feasibility of the system are explored in detail through a comprehensive experimental investigation employing an innovative and practical approach to design and examine the integrated hybrid solar MD unit. This unique system was designed, constructed, and tested under dynamic outdoor conditions, during the summer season in Melbourne, to assess the integration strategy and evaluate its long-term operational performance. The performance of the integrated PV/T-MD system was evaluated using two commercial hydrophobic membranes with different pore sizes under various outdoor conditions and PV/T fluid flow rates. Key performance metrics analysed include solar irradiance intensity, temperature profiles of the PV/T panel and MD module, power, current and voltage profiles of the unit components, permeate flux, specific water productivity (SWP) and the thermal (ηthermPV/T) and electrical (ηelecPV/T) efficiencies, along with the gained output ratio (GOR). Comprehensive experimental assessments in indoor and outdoor settings were undertaken to confirm the technical viability of the system. The study demonstrates the feasibility of such systems through real-world trials and addresses key challenges in energy efficiency and internal heat recovery. The experimental trials demonstrated permeate flux values ranging between 8–16 kg/m2h outdoors, compared to 22–30 kg/m2h indoors, reflecting the impact of fluctuating environmental conditions. The system’s power consumption was measured 130–140 W, about one-third of the PV/T power rating. The maximum power generation reached 280 W in battery charging mode. achieving a maximum ηthermPV/T of 20 % and an ηelecPV/T of 18 %. Additionally, the cooling effect of the PV/T panel resulted in a 1.5–2 °C temperature reduction, improving electrical power output by 0.8–1.2 %. The findings highlight the significant potential of the system in providing reliable and efficient freshwater production in remote, off-grid communities, offering a scalable solution to address global water scarcity challenges.

Charged atomization characteristics and influencing factors analysis of aviation kerosene/ethanol blended fuel

ABSTRACT In this study, an experimental apparatus for charged atomization was established to explore the charged atomization modes of blended aviation kerosene/ethanol fuels and the corresponding evolution of atomization modes for E0 (100% aviation kerosene), E10 (10% ethanol + 90% aviation kerosene), E30 (30% ethanol + 70% aviation kerosene), and E50 (50% ethanol + 50% aviation kerosene) blends with varying applied voltages. Furthermore, the influence of the ethanol blending ratio, electrode separation distance, fuel flow rate, nozzle diameter, and other influencing factors on the charged atomization characteristics was investigated. Experimental results within the 0 to 10 kV voltage range revealed that as the voltage increased, the E0 fuel transitioned from droplet formation to spindle shape without exhibiting atomization, while the E10, E30, and E50 fuels displayed a sequential progression of atomization modes including droplet, micro-droplet, pulsed-jet, cone-jet, deflected-jet, and multi-jet configurations. As the ethanol blending ratio increases, the critical voltage required for the transition to atomization mode decreases, while the atomization cone angle gradually increases. The average atomization cone angles are 38.98°, 49.97°, and 66.83° for E10, E30, and E50, respectively. When the nozzle diameter is reduced from 1.05 mm to 0.3 mm, the charged atomization cone angle does not change significantly. With increasing fuel flow, the critical voltage for the atomization mode transition increases progressively, and the charged atomization cone angle also increases. The average atomization cone angles are 65.80°, 88.45°, 93.04°, 103.63°, and 110.97°, respectively.

On the Effect of Current Stabilizer on Dynamics of a Small Hybrid Wind Power Generator

The use of small wind power generators remains quite relevant. In particular, they can be efficient for charging batteries in remote locations where there is no centralized power supply (including in the Arctic, Far East, etc.). They can also be used as part of missions to planets with atmospheres. One of the promising design solutions for a small wind power generator with a vertical axis of rotation is a hybrid device. It consists of two wind turbines that have a common axis of rotation: external (Darrieus wind turbine) and internal (Savonius rotor). This scheme represents a compromise between the relatively high power coefficient of the Darrieus turbine and the good startup characteristics the Savonius rotor. It is known that one of the typical battery charging modes is constant current charging. Here we consider a hybrid installation, the generator of which is connected to a current stabilizer. The load is simulated with an active resistance. It is assumed that the generator is a DC generator. A closed mathematical model of the studied system is constructed. The aerodynamic load is described using the quasi-steady approach. It is assumed that the characteristic time of electrical processes is much smaller than the characteristic time of mechanical processes. The influence of load resistance on the behavior of the system is investigated. It is shown that, under certain conditions, several steady modes (up to five) exist in the system. In this case, at least two of them are attracting. Therefore, the hysteresis of the angular speed of the steady mode is possible when the load resistance changes. It should be noted that in a number of situations, an unstable steady mode (which corresponds to a lower angular speed of the turbine than a stable one) may be preferable (for example, to reduce the load on bearings). In this regard, a resistance control strategy has been proposed to ensure stabilization of the unstable stationary regime.

Operational vehicle state of health estimation framework based on local-global attention mechanism

The state of health (SOH) of electric vehicles directly correlates with operational safety and driving range. Accurate assessment of SOH is crucial for improving the safety and reliability of electric vehicles. This article proposes a novel SOH estimation framework based on the local-global attention mechanism (L-G-A) and real-world incomplete charging processes. Firstly, an analysis of the charging modes is conducted. Specifically, the average value of capacity is calculated for multiple constant current segments as the reference capacity, and the effect of ambient temperature on charging efficiency and battery capacity is considered. Subsequently, aging characteristics of interval capacities are extracted through incremental capacity analysis. To investigate the impact of different usage patterns on battery degradation during daily use by vehicle owners, the historical cycle charging segments are categorized based on segment similarity. Then, the L-G-A model is established by utilizing convolutional layers to extract local-scale temporal information and computing global attention scores based on degradation information between each segment and across classes. The experimental results demonstrate that the proposed framework has the highest estimation accuracy with mean absolute error and root mean square error of 0.069 % and 0.084 %, respectively, which outperforms the traditional LSTM, CNN, and SVR models in terms of accuracy of SOH evaluation.

Profit enhancement and imbalance cost reduction with solar PV-fuel cell-EV hybrid system in a wind-integrated day-ahead power market

Abstract In a wind-integrated deregulated network, the wind farm must intimate the power-generating capacity bids to the market controller at least one day before it begins operations. The wind farm's bid submissions are based on the estimated wind speed (EWS); nevertheless, slight differences between the real wind speed (RWS) and the EWS will result in penalties or incentives imposed by the Independent System Operator (ISO). This occurrence is known as the power market imbalance cost, and it has a direct influence on the system's profitability. To mitigate this effect, solar PV and fuel cell storage technologies are used with the wind farm to enhance system profit by offsetting the negative effects of the imbalance cost. Here, solar PV and fuel cells are used to function in the required time i.e. operate in the charging mode when the RWS is greater than the EWS and in discharging mode when the EWS is greater than the RWS to balance the power supply in the grid as to fulfill the power bidding conditions. Furthermore, the study focuses on minimizing potential system risks, which have been assessed using several risk assessment tools such as Value-at-Risk (VaR) and Cumulative Value-at-Risk (CVaR). The work was conducted using an IEEE 14 bus test system. Initially, the solar PV-fuel cell system supplies power to meet local needs, and the remaining energy is sent to the grid to maximize the system's profit. Electric vehicles (EVs) have also been incorporated to maximize the system economy in more quantities and to reduce the system risk further as compared to the solar PV-fuel cell operation. Three different optimization methods, i.e. AGTO (Artificial Gorilla Troops Optimizer Algorithm), ABC (Artificial Bee Colony Algorithm), and SQP (Sequential Quadratic Programming), were used in a comparative analysis to assess the effectiveness of the proposed approach.

Optimization of PV benefits for electric vehicles charging station using a deterministic method and fuzzy inference system method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of −863.4c€/day, whereas the FIS method delivers greater savings of −898.7c€/day. For fast charging, the deterministic approach yields savings of −524.6c€/day, while the FIS method results in even higher savings of −540.7c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20 %, compared to 81.60 % with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

Using multi‐component carbon composite as current collector in ultra‐battery

AbstractThis study introduces a multi‐component composite as a substitute for lead grids in ultra‐battery structures. The presented composite decreases the weight and simultaneously extends the cycle life of the traditional lead‐acid battery. Overall, UBZCP (the sample containing metal oxides and polyaniline), as the best sample, increases the cyclic stability by 3.3 and 1.4 times under high rate partial state of charge (HRPSoC) mode, compared to Native and UB450 samples. Metal oxides predominantly enhance the electrical conductivity of carbon‐based composites due to catalytic effects in the reduction of graphene oxide during heat treatment. Poly aniline shows considerably positive effect of electrochemical performance due to the high pseudo‐capacitive properties, high interaction of lead ions and the nitrogen in the polymer chains and accordingly hydrogen evolution reaction (HER) inhibition. The synergistic effect of polyaniline and metal oxides makes the best performance in the UBZCP sample achieve. Also, the UBZCP sample represented an enhancement of about 1.21 and 1.39 times in battery life and specific capacity under deep discharge mode compared to the Native sample. As a result, the introduced multi‐component composite making a 3D conductive network, facilitates Pb/PbSO4 reaction reversibility, and reduces the lead sulfate crystal size.

Enhancing energy control for a hybrid system comprising a wind turbine, diesel generator, and storage systems

The increase in demand for electrical energy has made renewable energy sources, such as wind power, increasingly attractive. However, intermittency and variability represent the major problems of these types of energies. The hybridization of several energy sources allows to have a reliable and efficient supply system. This paper was interested in the control of a hybrid energy system, which includes a wind turbine, storage systems (batteries and super-capacitors) and a conventional diesel generator, with the aim of generating constant power. A control strategy is developed taking into account the various constraints of the hybrid system. Both of storage systems as well as the diesel generator operate according to the modes presented in the supervision algorithm to absorb and compensate the fluctuation of the wind energy and reach to a constant energy flow. The super-capacitor operates before the battery to absorb the power peaks in both charge and discharge modes. The hybrid system is simulated using MATLAB/Simulink software, and the results are provided in order to show the effectiveness of this control strategy.

A quasi-two-stage trans-critical CO2 heat pump with in-cycle thermal storage for performance enhancement

To decarbonise the heating sector, there is a strong demand for environmentally friendly and cost-effective heating technologies. Trans-critical CO2 heat pumps offer a sustainable alternative to traditional vapor compression cycles that rely on environmentally harmful synthetic refrigerants. However, a significant challenge within trans-critical CO2 heat pumps is their high throttling loss during expansion, leading to substantial flash gas production and subsequently higher recompression power consumption. In this paper, we apply our Flexible Heat Pump cycle concept to trans-critical CO2 heat pumps to address this issue. A thermal storage unit is introduced into a traditional trans-critical CO2 heat pump cycle to recover heat from the hot super-critical CO2 leaving the gas cooler during charging mode. Once it is fully charged, the stored heat is then used as a temporary heat source for the heat pump’s operation during the discharging mode. Since the recovered heat has a higher temperature than ambient. The resultant flexible trans-critical CO2 heat pump (FlexiCO2-HP) reduces throttling losses and the compressor’s power consumption, leading to a higher average COP (Coefficient of Performance). The obtained results of the FlexiCO2-HP are compared against a single-stage trans-critical CO2 heat pump. For gas cooler temperatures ranging from 35 °C to 60 °C and across various discharge pressures, its COP is potentially 10 % to 40 % higher than the standard cycle under ideal conditions. In addition, the proposed system demonstrates a lower optimal gas cooler pressure across different gas cooler temperatures. Furthermore, this study compares the FlexiCO2-HP with other trans-critical CO2 systems, including those with expansion work recovery and two-stage configurations. The results demonstrate that the FlexiCO2-HP offers improvements in COP for most conditions, though not all. It has the potential to achieve a comparable COP when compared with ejector-based and expander-based heat pumps or two-stage systems, but it is much simpler in design without introducing an extra expander or compressor.

A Multi-Functional Integrated Onboard Charger for Dual-Motor Driving EVs

In this paper, to achieve versatile, cost-effective charging for dual-motor EVs, a multi-functional integrated onboard charger is constructed using a dual-motor driving system. In the driving mode, a five-phase flux-switching permanent-magnet (FSPM) motor powers the front, while a three-phase FSPM motor drives the rear. While in the charging mode, different topologies are adopted for different application scenarios, such as the single-phase AC charging mode, the three-phase AC charging mode, and the DC charging mode. The five-phase FSPM motor and its inverters serve as a boost-based AC/DC converter in both single-phase and three-phase AC charging modes, transforming grid power to DC. In the DC charging mode, they are reconfigured to function as a buck converter. During the three-phase AC charging mode, the three-phase FSPM motor and its inverters take on the role of a rear-stage buck converter. They function to regulate the rectified DC voltage, ensuring it meets battery charging needs. The performance of the integrated charger is validated through simulation and experiment results.

Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control

This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV charging process during grid faults. Additionally, PV arrays are typically designed to extract maximum power, leading to over-current or over-voltage situations that compromise the safety of the charging infrastructure and the EV. Furthermore, these systems often require multiple power electronics converters, increasing complexity and costs while reducing overall system efficiency. To overcome these limitations, the proposed algorithm dynamically switches between on-grid and off-grid modes based on real-time weather conditions, grid availability, and the state of charge of the battery electric vehicle (BEV). This approach maximizes PV power utilization, minimizes grid dependency, and enhances BEV charging performance while prioritizing EV safety and ensuring an uninterrupted power supply. It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. Two distinct operating modes, adaptive charging and fast charging, are introduced, each integrated with dedicated model predictive controllers (MPC) to achieve specific control objectives. Semi-experimental simulations using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335 demonstrate that the station manager significantly improves performance over conventional methods under various irradiance levels. Moreover, numerical results demonstrate the superiority of the proposed MPC-based approach over traditional controllers such as Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) in different charging modes. For instance, the MPC controller achieved an Integral Absolute Error (IAE) of 11.45%, lower than that of the PID controller, and 4.3% lower than the SMC controller.

Two-Stage Multiple-Vector Model Predictive Control for Multiple-Phase Electric-Drive-Reconstructed Power Management for Solar-Powered Vehicles

Electric-drive-reconstructed onboard chargers (EDROCs), also known as electric-drive-reconstructed power management systems, are a promising alternative to conventional onboard chargers due to their characteristics of low cost and high power density. The model predictive control offers a fast dynamic response, simple implementation, and the ability to control multiple targets simultaneously. In this paper, a two-stage multi-vector model predictive current control (MPCC) of a six-phase EDROC for solar-powered electric vehicles (EVs) is proposed. Firstly, the topology for the EDROC incorporating a six-phase symmetrical permanent magnet synchronous machine (PMSM) is introduced, and the operation principles of the DC charge mode, the drive mode, and, especially, the in-motion charge mode are analyzed in detail. After that, a two-stage multi-vector MPCC method is proposed by using the multi-vector MPC technique and designing a two-stage MPC structure to eliminate the regulation of the weighting factor of the MPC. Finally, the effectiveness of the proposed method is verified on a self-designed 2 kW EDROC platform.

Smart home power management algorithm using real-time model predictive control for a stand-alone PV system with battery energy storage

A smart home power management system is critical for stand-alone home-photovoltaic (HPV) with battery energy storage. Existing approaches often focus on maximizing power extraction from PV systems without considering real-time power adjustments or battery state of charge (SoC), which can lead to over-current or over-voltage issues that damage the battery. To address these limitations, this paper proposes a home power management algorithm that dynamically adjusts the power flow between the PV system, home loads, and the battery based on real-time system power measurements and battery SoC. This dynamic adjustment ensures an uninterrupted power supply to the home loads while maintaining battery safety. The proposed home manager algorithm features multiple operating modes: Maximum power point (MPP) charging, partial charging, fast charging, float charging, and partial discharging. Each mode is integrated with its dedicated model predictive controller (MPC) to achieve its specific control objective. Finally, through a semi-experimental simulation using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335, testing under various irradiance levels shows that the home manager achieves significant improvement over conventional approaches. For instance, over the MPP mode it achieve a power efficiency of 99.36% with a 2.05% SoC increase, while fast charging mode resulted in 87.44% efficiency with an 11.2% SoC increase. However, float charging mode maintained an 87.27% efficiency with a 2.6% SoC increase, and partial discharging led to a 1.3% SoC decrease. The overall battery efficiency reached 96.45%, demonstrating the algorithm’s ability to optimize power flow, ensure a reliable energy supply, and maintain battery safety under varying weather conditions.

Development of water-based micro-particle enhanced phase change material for cool thermal energy storage systems

ABSTRACT Building accounts over one-third of global energy consumption, mainly for cooling/heating the space, contributing to enormous emission of greenhouse gases. Extensive usage of air-conditioning system in tropical countries demands cool thermal energy storage system for energy management that essentially demands development of novel energy storage materials. In this study, micro-particle enhanced phase change materials (MePCMs) were prepared through two-step method with deionized water as base PCM and natural graphite flakes (NGF) as additive. Heat transfer characteristics of the MePCMs were experimentally analyzed during solidification in a spherical capsule. High specific surface area of the NGF creates more nucleation sites that lead to elimination of subcooling in the MePCM. As a result of enhanced thermal conductivity of MePCM, solidification duration was reduced to 40% with 5 wt.% NGF, compared to base PCM. Accelerated mode of charging was seen in MePCMs, storing 96% of energy during 70% of corresponding solidification duration. In addition to the above fascinating advantages, abundant availability and low cost of the NGF make the MePCM as potential energy storage material for space cooling applications.