This paper investigates how a data-driven surrogate model developed for Synchronous Reluctance Motors can be systematically extended to Permanent Magnet Assisted Synchronous Reluctance Motors, enabling fast dynamic performance evaluation. Initially, the semi-analytical formulation used in this work is introduced, describing the inductance structure. In this formulation, the inductance matrix of the time domain equations is represented by a finite element based dataset accounting for inductance harmonics, rotor position dependency, and magnetic saturation effects. The effect of the permanent magnets is then introduced through the permanent magnet flux contribution, determined from induced stator voltage waveforms and incorporated into the model equations. Preliminary results are presented demonstrating the computation of the electromagnetic torque ripple and its agreement with the original finite element simulations used to generate the dataset.

The rapid growth of electric vehicle ownership and advancements in vehicle-to-grid (V2G) technologies have created an urgent demand for bidirectional charging-discharging interfaces. Wireless power transfer (WPT) technology, known for its convenience, safety, and flexibility, is a promising solution for energy transfer between vehicles and the grid. This paper presents the design and demonstration of a highly interoperable and highefficiency bidirectional WPT system, addressing key challenges such as wide voltage output adaptation, multipower level compatibility, and efficient operation over a broad power range. The front-end converter uses a power module combining a three-phase fully controlled rectifier and a cascaded buck converter to provide a wide DC voltage range. Modular activation technology ensures the grid interface operates efficiently under varying power demands. For the bidirectional inductive power transfer (BIPT) link, an integrated scheme for the resonant networks in the ground assembly (GA) with cross-frequency compatibility is proposed, and its performance is validated through calculations and simulations. A bidirectional power flow control strategy is implemented, with voltage regulation and operation mode switching as the main method. Experimental results demonstrate interoperability between the same grid-side equipment and different vehicle-side equipment rated at 6, 11, and 30 kW. Under specified operating conditions at the aligned position, the system achieves a grid-to-battery efficiency from 91.7% to 94.3%, and a battery-to-grid efficiency ranging from 89.5% to 93.5%.

Sodium-ion batteries (SIBs) have emerged as a viable substitute for lithium-ion batteries (LIBs) in electric vehicles (EVs) due to the growing need for sustainable energy solutions. A comprehensive modeling framework for SIBs designed especially for electric vehicle applications is presented in this paper, with a focus on how these batteries integrate with wireless communication and intelligent transportation systems (ITS). The thermal and electrochemical models of SIBs, their possible use in vehicle-to-everything (V2X) systems, and the wireless state-of-charge (SoC) monitoring via real-time communication protocols are investigated. Simulation results demonstrated the efficiency of SIBs in vehicular applications, their compatibility with wireless data transfer protocols, and their contribution to energy optimization in ITS. The paper concludes with insights into the future of SIBs in EVs and wireless transportation ecosystems.

Fossil fuel depletion, environmental concerns, and energy efficiency initiatives drive the rapid growth in the use of electric vehicles. However, lengthy battery charging times significantly hinder their widespread use. One proposed solution is implementing battery swapping stations, where depleted electric vehicle batteries are quickly exchanged for fully charged ones in a short time. This paper evaluates the techno-economic feasibility and optimal design of a grid-connected hybrid wind–photovoltaic power system for electric vehicle battery swapping stations. The aim is to evaluate the viability of this hybrid power supply system as an alternative energy source, focusing on its cost-effectiveness. An optimal control model is developed to minimize the total life cycle cost of the proposed system while reducing the reliance on the utility grid and maximizing system reliability, measured by loss of power supply probability. This model is solved using mixed-integer linear programming to determine key decision variables such as the power drawn from the utility grid and the number of wind turbines and solar photovoltaic panels. A case study validates the effectiveness of this approach. The simulation results indicate that the optimal configuration comprises 64 wind turbines and 402 solar panels, with a total life cycle cost of ZAR 1,963,520.12. These results lead to an estimated energy cost savings of 41.58%. A life cycle cost analysis, incorporating initial investment, maintenance, and operational expenses, estimates a payback period of 5 years and 6 months. These findings confirm that the proposed hybrid power supply system is technically and economically viable for electric vehicle battery swapping stations.

ABSTRACT Eskom, South Africa's national power utility, is transitioning from centralised, large‐scale electricity coal generation to a more distributed, small‐scale inverter‐based renewable generation to reduce greenhouse gas emissions. This shift poses operational challenges, particularly in maintaining power system frequency stability, which relies on real‐time balancing of supply and demand. Traditionally, frequency stability has depended on accurate load forecasts, sufficient generation capacity, and energy reserves from large generators to handle disturbances. However, as the number of large generators decreases, energy reserves will also reduce, potentially compromising frequency stability. This paper introduces the concept of integrating small‐scale distributed generators to enhance both primary and secondary frequency control. By actively monitoring and managing these inverter‐based generators, while accounting for phase balancing and network congestion, the proposed system seeks to improve grid stability, minimise reliance on large generators, and mitigate the risk of secondary frequency drops within an unmanaged inverter‐based network (i.e. the high rate of change of frequency (RoCoF) may lead to inverter trips).

This paper presents an optimal power flow dispatching for a grid-connected photovoltaic-battery energy storage system under grid-scheduled load-shedding to explore solar energy sufficiently and to benefit the electric vehicle battery swapping station at the charging demand side. The proposed system comprises a solar photovoltaic system, a battery energy storage system, and an electric vehicle battery swapping station. The optimization problem is formulated as a multi-objective optimization problem in a discrete-time domain to minimize the operational costs associated with the power flow drawn from the utility grid and the wearing cost of the hybrid system due to the frequent charging and discharging of the battery energy storage system when charging the depleted EV battery. A linear programming method determines the optimal power flow in the proposed system to charge the depleted battery for electric vehicles. Simulation results show the effectiveness of the developed model by providing the optimal dispatch power flow at the electric vehicle battery swapping station at the charging demand side. The comparative analysis with related works further underscores the competitive performance of the proposed optimization approach in enhancing energy resilience and cost efficiency.

The global battery industry is experiencing significant growth, and this growth is predicted to continue and accelerate in the future. The cost of lithium (Li) and cobalt (Co) resources is rising as a result of growing applications and demand. Therefore, the abundance of sodium (Na) resources and their global distribution drive us to research Na-ion (Na+) batteries for immobile energy storage systems. The advancements of Na+-batteries are reported in this paper, primarily presenting earlier and current studies in contrast to those of Li-ion (Li+) battery energy storage systems. Despite the increasing global use of Li+-battery systems, academic research has largely overlooked Na+-battery technologies. This study explores and details the most promising applications for Na+-stationary battery systems. The approach consists of two steps. First, it involves a comprehensive review of existing literature focusing on the applications, profitability, and use cases. Second, the study provides an in-depth analysis of these use cases, emphasizing the key factors driving their adoption, the sources of value they offer, and the associated risks.

Inverter-based resources (IBRs) have low inherent inertia, making it difficult to maintain system stability especially with of their increasing penetration. However, Flywheel Energy Storage Systems (FESSs), combined with advanced inverter technologies like Grid-Forming (GFM) and Grid-Following (GFL) inverter sources, offer a promising solution for frequency regulation and stability support. This paper investigates the dynamic performance of a microgrid with integrated FESS operating in both GFM and GFL modes, focusing on their complementary roles in maintaining frequency stability. A coordinated control strategy using MPC is introduced to optimize the response of both GFM and GFL inverters. The results obtained by simulation in Matlab/Simulink and validation on Digsilent/PowerFactory show that the RoCoF increases with the increased IBR penetration. To balance these two IBR interfaces, the focus should be on the GFM-FESS. It should be tuned, which, in this investigation, leads to better frequency response with the same capacity, size, and location. This adaptability makes the FESS a highly effective tool in stabilizing low-inertia power systems and ensuring microgrid frequency regulation.

This paper focuses on the energy management and control strategy suited to improve system resilience through peer-to-peer energy sharing in a remote interconnected microgrid. In remote interconnected microgrids, system resilience can be an issue as the restoring workforces are not readily available to attend to system failures which may result in extended blackouts. Therefore, a novel and error-free energy management and control system is required to ensure the resilience of interconnected microgrids in remote areas. This energy management strategy is called the nested energy management and control system which is derived from strategically nesting or layering energy management systems of each microgrid in an interconnected microgrid system. In the proposed nested energy management strategy, the surplus energy existing in the inner-level energy management system is reflected as a resource and the deficit as a load to the outer-level energy management system. For this proposed concept, a mixed integer linear programming (MILP)-based problem formulation and algorithm is considered which takes into consideration the system operational constraints such as voltage, frequency, thermal limits and power quality are controlled whilst maintaining optimal nested peer-to-peer energy sharing. In this paper, MATLAB Simulink interfaced with HOMER Pro software is used to model and simulate the optimal peer-to-peer energy sharing amongst three interconnected microgrids.

An optimised design for an on-grid photovoltaic power supply system to be used in an electric vehicle battery swapping station is presented. How integrating photovoltaic generator systems with battery-swapping stations can enhance their sustainability, reliability, and cost-effectiveness is explored. The aim is to minimize the life cycle cost of the grid-connected photovoltaic power supply system and the cost of electricity purchased from the utility grid while maximizing its reliability constraints. Using mixed integer linear programming, the most optimal values for the decision variables were identified. The optimization results showed a total life cycle cost of R 362,934.25, an optimal energy consumption from the utility grid of 782.7 kWh, and an optimal number of 244 solar panels. This results in a daily energy saving of up to 60.01% compared to the baseline, and an economic benefit of R 1,034,273.25 over the project lifetime. Varying the weighing factor affects the multi-objective optimization sizing, and the optimal weighting factor is between 0.327 and 0.713 for the best cost-effectiveness.

In conventional DC microgrids (µGs) that have multiple parallel power sources connected to the DC bus, the problem of voltage regulation and current sharing is inherent. Therefore, control methods, such as droop control, have been mainly used in the primary and secondary control layers. While droop control allows current sharing between multiple parallel µG sources, it causes a deviation from the nominal voltage at the DC bus. To address this challenge, a secondary controller is proposed to simultaneously guarantee proportional current sharing while maintaining the DC bus voltage at the nominal value. A distributed approach is utilized in this paper to avoid the single-point failure of centralized controllers and enhance plug-and-play capability. A state-space approach is used to formulate the DC-µGs to aid controller design and analyze its stability. MATLAB/Simulink is utilized to simulate the formulated state-space model of the DC-µGs and the proposed secondary control strategy.

The battery swapping mode (BSM) for an electric vehicle (EV) is an efficient way of replenishing energy. However, there have been perceived operation-related issues related large-scale deployment of the BSM. How-ever, previous reviews have failed to examine the mathematical methods of the operation optimization process, which are highlighted in this work. The paper aims to provide a complete and systematic overview of the operation optimization approaches for EV battery swapping and charging stations. This work addresses the current operation mode of battery swapping networks and examines the optimization objectives, constraints, and mathematical programming methods. The paper highlights the motivations of different ownership models for establishing different objectives and discusses the merits and drawbacks of approaches in previous studies for different application scenarios. For the possible focus of future work, the paper details opportunities and chal-lenges of dynamic service pricing, battery-to-grid scheduling, and behavior scheduling. This review aids future research of battery charging and swapping station operation and vehicle scheduling, and provides a systematic and theoretical reference for model selection.

Battery Swapping Mode (BSM) has emerged as an effective approach for enhancing the daily driving range of electric vehicles (EVs), particularly in public application scenarios such as taxis and trucks. However, there remains unclear regarding the characteristics of BSM usage patterns and the distinctions compared to the fast-charging method. Thus, the primary objective of this paper is to present empirical evidence derived from real-world operational data. This evidence-based analysis delves into the dynamics of energy refueling behaviors and driving patterns associated with BSM. The insights gained from this study have the potential to inspire local policymakers to understand the real-world operation of the BSM and leverage historical insights to formulate effective developmental strategies.

This paper explores the major degradation characteristics of commercial lithium-ion battery cells with nickel --cobalt-aluminum-oxide (NCA) electrode during cyclic overcharging, and proposes non-destructive methods for detecting overcharging degradation failure. The experimental results show that battery capacity drops signifi-cantly with increasing overcharge depth and number of cycles especially during the first three cycles and when the charging termination voltage is set to 5 V. At the same time, the cell overcharge tolerance decreases with the cyclic overcharging. The combination of the electrochemical impedance spectroscopy and the incremental ca-pacity and differential voltage analysis is used to diagnose cell degradation during cyclic overcharging. Three main degradation modes are identified and quantified by extracting characteristic parameters such as internal resistance and peak, valley, and curve position changes of incremental capacity curves. It is concluded that loss of lithium inventory and loss of active materials are the most dominant degradation modes during cyclic over-charging. Besides, the sharp increase of the third peak on incremental capacity curves has been identified as a unique feature of overcharging degradation, which can be used for diagnosing cyclic overcharging-induced degradation for batteries with NCA cathode.

The doubly fed induction generator is commonly used in commercial wind turbines because of its simplicity and robustness; it is a wound rotor induction machine. It has a relatively small airgap which makes it more vulnerable to unbalanced magnetic pull. This paper presents a simulation model to investigate the ability of extra stator windings with p(m) +/- 1 pole-pairs to control the unbalanced magnetic pull (UMP). These auxiliary windings are inserted in the stator slots and they are used to produce counteracting flux that reduces the UMP. In the theory section, an UMP control matrix is fully developed that involves additional winding currents for obtaining active UMP control. The algorithm for controlling the UMP is then introduced. This uses vector control with stator flux orientation. Several simulations are carried out using MATLAB and Altair Flux 2D finite element analysis. These show a good reduction of UMP under open circuit and locked rotor tests. These models allow the study of the combined effects of radial forces with changes in the machine design. It was found that the average power absorbed by the auxiliary windings equals 13% of the total power losses in normal operation conditions.

The safe operation, control, and stability of standalone microgrids (MGs) are highly dependent on their coordination with the MG control center (MGCC). Due to the open communication channel between the MG and the MGCC, the measurement signals are vulnerable to cyber attacks that can compromise the stability of the system. In this article, a false data injection (FDI) attack on the frequency measurement of a standalone MG is considered to disrupt the stable operation of the MG. Therefore, an attack detection and identification method are proposed to protect the MG against the impacts of this attack. The proposed method is based on a dynamic state estimation technique that uses an unknown input observer (UIO) to estimate the MG states and generate a residual function that detects the presence of an FDI attack and triggers a detection alarm for attack isolation and mitigation. The robustness and practicability of the proposed method are demonstrated with real-time simulation results of a real-world MG system.

Battery swapping becomes popular because it can reduce energy refueling duration, regulate grid load, and extend battery life. Although substantial efforts have directed to the construction and operation of battery swapping stations (BSSs), there is still lack of a systematic and complete review on the topic. Therefore, this paper provides a comprehensive literature review on the siting and sizing and operation mechanisms of the BSS. The optimization objectives, constraints and algorithms are sorted and surveyed with their merits and drawbacks expounded in details. The synergistic optimization of siting and sizing and the collaborative scheduling with microgrids and routing of EVs are also highlighted and discussed in details. Moreover, the major challenges and future research directions for BSSs are also pointed out. (c) 2022 Elsevier Ltd. All rights reserved.

Electric vehicle (EV) performance in terms of the available driving range per charge and the energy consumption rate continuously degrades during its service life. Quantitative assessments of EV performance degradation play an important role in EV residual value analysis, battery management, and battery recycling. However, EV per-formance degradation is highly sensitive to both ambient temperature and battery aging states; coupled factors make its quantification challenging. Here, a novel big data-driven decoupling framework is proposed to inves-tigate the partial relationships between EV performance degradation and each individual variable (e.g., tem-perature and total driving distances). The core innovation involves the decoupling process that can enable real -world and large-scale degradation assessments. The basic functionality of the decoupling is achieved by an iterative learning framework where different machine learning-based models can communicate with each other. It achieves the advantages of unsupervised training and high performance; the mean absolute error can be controlled less than 0.1 in the model validation of EV ranges. Its effectiveness is verified using different real -world EV datasets. By utilizing the framework, the changes in the range and energy consumption of EVs across 10 urban areas in China are assessed. The results show that the range and energy consumption rate of EVs are more greatly influenced by ambient temperature than by battery aging. Less consideration of variable decoupling may yield misleading results in EV performance analysis. Our proposed framework opens avenues for quantifying EV performance degradation via real-world EV data, which is critical to onboard and cloud-based EV research.

Individual remote agro-based micro-grid is prone to reliability and resilient instability issues due to large sudden load or generation fluctuations. Therefore, it is important to integrate several micro-grids to solve the issue of reliability. An interconnected micro-grid system takes advantage of various complementary energy sources and effectively coordinates the energy sharing among the neighbouring micro-grids to improve the stability, reliability, and energy efficiency of the system in case of loss or insufficient power supply from one micro-grid. Control of energy management and communication for inter-micro-grid becomes complex and challenging in these areas due to the excess demand of agro-based consumer loads. In this paper, a decision-making algorithm that provides smart solution in interconnecting several neighbouring micro-grids to optimally share the supply is developed. A case study is presented and HOMER Pro software is used to optimize three proposed micro-grids and ensure optimal energy sharing.

Electrochemical energy storage systems are fundamental to renewable energy integration and electrified vehicle penetration. Hybrid electrochemical energy storage systems (HEESSs) are an attractive option because they often exhibit superior performance over the independent use of each constituent energy storage. This article provides an HEESS overview focusing on battery-supercapacitor hybrids, covering different aspects in smart grid and electrified vehicle applications. The primary goal of this paper is to summarize recent research progress and stimulate innovative thoughts for HEESS development. To this end, system configuration, DC/DC converter design and energy management strategy development are covered in great details. The state-of-the-art methods to approach these issues are surveyed; the relationship and technological details in between are also expounded. A case study is presented to demonstrate a framework of integrated sizing formulation and energy management strategy synthesis. The results show that an HEESS with appropriate sizing and enabling energy management can markedly reduce the battery degradation rate by about 40% only at an extra expense of 1/8 of the system cost compared with battery-only energy storage.