Demand For Electric Vehicles Research Articles

Dual layer energy management model for optimal operation of a community based microgrid considering electric vehicle penetration

This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

Enhanced Structure/Interfacial Properties of Single‐Crystal Ni‐Rich LiNi0.92Co0.04Mn0.04O2 Cathodes Synthesized Via LiCl‐NaCl Molten‐Salt Method

Arising from the increasing demand for electric vehicles (EVs), Ni‐rich LiNixCoyMnzO2 (NCM, x + y + z = 1, x ≥ 0.8) cathode with greatly increased energy density are being researched and commercialized for lithium‐ion batteries (LIBs). However, parasitic crack formation during the discharge–charge cycling process remains as a major degradation mechanism. Cracking leads to increase in the specific surface area, loss of electrical contact between the primary particles, and facilitates liquid electrolyte infiltration into the cathode active material, accelerating capacity fading and decrease in lifetime. In contrast, Ni‐rich NCM when used as a single crystal exhibits superior cycling performances due to its rigid mechanical property that resists cracking during long charge–discharge process even under harsh conditions. In this paper, we present comparative investigation between single crystal Ni‐rich LiNi0.92Co0.04Mn0.04O2 (SC) and polycrystalline Ni‐rich LiNi0.92Co0.04Mn0.04O2 (PC). The relatively improved cycling performances of SC are attributed to smaller anisotropic volume change, higher reversibility of phase transition, and resistance to crack formation. The superior properties of SC are demonstrated by in situ characterization and battery tests. Consequently, it is inferred from the results obtained that optimization of preparation conditions can be regarded as a key approach to obtain well crystallized and superior electrochemical performances.

A Financial Analysis and Valuation of Tesla

The pressing issue of global warming, fueled by excessive carbon dioxide emissions, has spurred global concern. Researchers highlight the potential of new energy vehicles, particularly electric cars, in significantly mitigating greenhouse gas emissions. As demand for electric vehicles rises, attention turns to companies like Tesla, spearheading this transformative industry. This study delves into Tesla's financial landscape in 2023, juxtaposing it against other electric vehicle manufacturers, aiming to unveil Tesla's comparative advantages. Leveraging data sourced from Yahoo Finance, the study employs a comprehensive financial analysis, focusing on liquidity, solvency, profitability, and investment metrics. Results underscore Tesla's remarkable performance across these dimensions, showcasing its impressive profitability, operational prowess, financial stability, and investment appeal relative to its competitors. This report stands poised to guide investors intrigued by Tesla's potential, offering valuable insights into the company's financial health and positioning within the burgeoning electric vehicle sector. Through such analysis, stakeholders can make informed decisions in navigating the dynamic landscape of sustainable transportation investments.

A Financial Analysis and Valuation of Electric Vehicle Companies

This paper examines the electric vehicle (EV) industry, with a particular focus on Tesla, NIO, and BYD. Tesla is a leading player thanks to its efficient operations and diverse product lineup, making it a preferred choice for investors. NIO, despite having a wide range of products, faces profitability challenges, which may discourage investors. BYD impresses with its consistent profitability and significant market share in China. Financial metrics such as net profit margin, operating margin, and asset turnover are analyzed to assess the performance and appeal of each company to investors. Tesla's operational efficiency, demonstrated by its strategically located Superfactories in major markets, reinforces its industry leadership. Its diverse product range and global market reach inspire investor confidence. On the other hand, NIO grapples with negative profit margins and limited production capacity, lessening its appeal to investors. BYD's steady profitability and considerable market share in China enhance investor confidence. Despite intense competition and market volatility, all three companies are poised to capitalize on the growing demand for EVs, driven by global sustainability efforts. In conclusion, Tesla and BYD appear as appealing investment prospects due to their profitability and market dominance, while NIO struggles to attract investors. Tesla's innovative production strategies and extensive market reach place it at the forefront of the EV industry, likely drawing more investors in the future.

Electric vehicle fast charging station energy management system for radial distribution network with a photo-voltaic distributed generator (PV-DG)

As the demand for electric vehicles (EVs) increases in all countries, automobile companies are launching different types of EVs. Installation of EVCS in the present power system network without modification can cause an extra burden on the present network. However, this extra burden can be reduced if EVCS gets a supply of renewable energy resources. This paper proposes energy management schemes by installing a photo-voltaic distributed generator (PV-DG) and energy storage system (ESS) at an EVCS. An IEEE-33 bus system for the Saudi Arabia United Kingdom rural location has been selected to introduce real-time energy management schemes. In this radial distribution network (RDN), three EVCS of different capacities are installed at different locations to maintain the stability of the network. The power flow program is used in the MATLAB environment to check the stability of the network. Accurate solar data, traffic patterns, and EV charging patterns in Saudi Arabia are collected and used for energy management at EVCS. Three cases are used for comparison: EVCS annual charges, EVCS annual benefits, and grid annual fuel charges. All proposed cases are also simulated in MATLAB using MATPOWER6 to check the impact on voltage profile and power line flows.

Dynamic pricing strategy for efficient electric vehicle charging and discharging in microgrids using multi-objective jaya algorithm

The rising demand for electric vehicle (EV) charging is spurring their increased integration into microgrids. With significant advancements, EVs have become widely adopted and integrated into various settings for charging/discharging. EVs integrated with the microgrids possess the capability to serve as variable loads and the various energy suppliers present it as a dual opportunity. However, a primary challenge in EV deployment lies in efficiently managing charging stations (CSs) to minimize waiting times for users and reduce charging costs for EV owners. In addressing these challenges require consideration of dynamic pricing mechanisms and the diverse characteristics of EVs to achieve optimal scheduling. A novel approach that combines dynamic pricing strategies with optimized scheduling for effective EV charging operations using multi-objective Jaya algorithm. To evaluate its performance, we conducted a numerical experiment using real-time data and the Nissan Leaf model EV. The results demonstrate that our multi-objective Jaya-based approach outperforms existing methods by achieving a remarkable cost saving rate of 16.013% and an average profit of ₹ 243.6331 per kilowatt-hour with a network convergence time of 112 s. Also, our proposed algorithm provides a correlation between minimized EV charging costs and maximized EV aggregator profits. These findings validate the effectiveness and practical applicability of our proposed EV scheduling algorithm in real-world scenarios.

Progress and Prospect of Sn‐based Metal‐organic Framework Derived Anode Materials for Metal‐ion Batteries

AbstractIn order to realize the growing demand for superior energy storage devices and electric vehicles, commercial anode candidates for next‐generation rechargeable batteries need to meet the characteristics of low cost, high energy density, high capacity, and stable performance. The emerging tin‐based anodes show great potential for high performance metal‐ion battery anodes due to their high theoretical capacity, low cost, green harmless and high safety. Tin based anode materials include tin gold based materials, tin alloy materials, tin based oxides, tin based phosphide, tin based sulfides, multi‐component composite materials, etc. However, the change in volume and structure of tin‐based anode materials during the cycle has become the biggest obstacle to its development. Metal‐organic frameworks (MOFs) provide a wide range of possibilities for achieving high rate capacity and excellent cycle stability by finely regulating the structure and composition of tin‐based materials at the molecular level. The latest progress of tin‐based materials derived from MOFs as anode materials for metal‐ion batteries (including lithium ion batteries, sodium ion batteries, potassium ion batteries, magnesium ion batteries) was reviewed in this paper. Firstly, the preparation method and morphology control of tin‐based MOF are briefly introduced, and the structural characteristics, storage mechanism and modification of tin‐based MOF derived materials are emphatically discussed. Finally, we summarized the existing modification measures and challenges of these anode materials, and put forward the prospect of the future.

Optimal planning for electric vehicle fast charging stations placements in a city scale using an advantage actor-critic deep reinforcement learning and geospatial analysis

The transition to Electric Vehicles (EVs) for reducing urban greenhouse gas emissions is hindered by the lack of public charging infrastructure, particularly fast-charging stations. Given that electric vehicle fast charging stations (EVFCS) can burden the electricity grid, it is crucial for EVFCS to adopt sustainable energy supply methods while accommodating the growing demands of EVs. Despite recent research efforts to optimize the placement of renewable-powered EV charging stations, current planning methods face challenges when applied to a complex city scale and integrating with renewable energy resources. This study thus introduces a robust decision-making model for optimal EVFCS placement planning integrated with solar power supply in a large and complex urban environment (e.g., Chicago), utilizing an advantage actor-critic (A2C) deep reinforcement learning (DRL) approach. The model balances traffic demand with energy supply, strategically placing charging stations in areas with high traffic density and solar potential. As a result, the model is used to optimally place 1,000 charging stations with a random starting search approach, achieving total reward values of 74.30 %, and estimated the capacities of potential EVFCS. This study can inform the identification of suitable locations to advance the microgrid-based charging infrastructure systems in large urban environments.

Ensuring Sustainable Grid Stability through Effective EV Charging Management: A Time and Energy-Based Approach

The rise of electric vehicles (EVs) has significantly transformed transportation, offering environmental advantages by curbing greenhouse gas emissions and fossil fuel dependency. However, their increasing adoption poses challenges for power systems, especially distribution systems, due to the direct connection of EVs with them. It requires robust infrastructure development, smart grid integration, and effective charging solutions to mitigate issues like overloading and peak demand to ensure grid stability, reliability, and sustainability. To prevent local equipment overloading during peak load intervals, the management of EV charging demand is carried out in this study, considering both the time to deadline and the energy demand of EVs. Initially, EVs are prioritized based on these two factors (time and energy)—those with shorter deadlines and lower energy demands receive higher rankings. This prioritization aims to maximize the number of EVs with their energy demands met. Subsequently, energy allocation to EVs is determined by their rankings while adhering to the transformer’s capacity limits. The process begins with the highest-ranked EV and continues until the transformer nears its limit. To this end, an index is proposed to evaluate the performance of the proposed method in terms of unserved EVs during various peak load intervals. Comparative analysis against the earliest deadline first approach demonstrates the superior ability of the proposed method to fulfill the energy demand of a larger number of EVs. By ensuring sustainable energy management, the proposed method supports the widespread adoption of EVs and the transition to a cleaner, more sustainable transportation system. Comparative analysis shows that the proposed method fulfills the energy needs of up to 33% more EVs compared to the earliest deadline method, highlighting its superior performance in managing network loads.

Online Spatial-Temporal EV Charging Scheduling with Incentive Promotion

The growing adoption of electric vehicles (EVs) has resulted in an increased demand for public EV charging infrastructure. Currently, the collaboration between these stations has become vital for efficient charging scheduling and cost reduction. However, most existing scheduling methods primarily focus on recommending charging stations without considering users’ charging preferences. Adopting these strategies may require considerable modifications to how people charge their EVs, which could lead to a reluctance to follow the scheduling plan from charging services in real-world situations. To address these challenges, we propose the POSKID framework in this paper. It focuses on spatial-temporal charging scheduling, aiming to recommend a feasible charging arrangement, including a charging station and a charging time slot, to each EV user while minimizing overall operating costs and ensuring users’ charging satisfaction. The framework adopts an online charging mechanism that provides recommendations without prior knowledge of future electricity information or charging requests. To enhance users’ willingness to accept the recommendations, POSKID incorporates an incentive strategy and a novel embedding method combined with Bayesian personalized analysis. These techniques reveal users’ implicit charging preferences, enhancing the success probability of the charging scheduling task. Furthermore, POSKID integrates an online candidate arrangement selection and an explore-exploit strategy to improve the charging arrangement recommendations based on users’ feedback. Experimental results using real-world datasets validate the effectiveness of POSKID in optimizing charging management, surpassing other strategies. The results demonstrate that POSKID benefits each charging station while ensuring user charging satisfaction.

Environmental and economic evaluation of urban building-integrated photovoltaic and electric vehicle system

Facade photovoltaic (FaPV) panels can significantly enhance the capability of building-integrated photovoltaic (BIPV) systems. This study presents a comprehensive analytical framework for optimizing the operation of BIPV and electric vehicle (EV) integrated systems and evaluating their environmental and economic benefits. The Energy-Environmental-Economic (3E) parameters are measured for alternative scenarios, and sensitivity analyses are applied to validate the impacts of changes in major parameters on BIPV-EV system's performance in Xiong'an New Area (XANA). The simulation results demonstrate that BIPV-EV systems can effectively reduce CO2 emissions and electricity costs. Specifically, the installation of FaPV can increase power generation by up to 67.60 % compared to the BIPV system utilizing stand-alone rooftop photovoltaic (RPV). Regarding urban decarbonization, the adoption of BIPV system can reduce CO2 emissions by 41.91 % and 34.99 % in the short- and long-term, respectively. The evaluation of the levelized cost of energy (LCOE) across different scenarios reveal that the configurations of RPV + FaPV (SE)-EV system and RPV + FaPV (SW)-EV system are the most cost-effective options in both the short- and long-term, where BIPV contributes to 48.10 % and 31.90 % of the total electricity generation, respectively. In terms of EV power consumption, the BIPV-EV system can decrease the grid sell ratio by 15.38 % compared to the stand-alone BIPV scenario. Compared to the BIPV system with typical RPV, the appropriate utilization of FaPV can significantly increase the potential of a BIPV system and mitigate the additional electricity demand for EVs. For cities with nascent BIPV and EV markets, the proposed framework can be a strategic tool for urban planners to design BIPV-EV systems, with the objective of enhancing sustainability and energy efficiency.

Bottom-up assessment of air quality management strategies in Vijayawada, India: Emissions inventory, atmospheric capacity, and policy scenarios

A bottom-up approach evaluates air quality management strategies in Vijayawada, India, a rapidly urbanizing city facing declining air quality. Detailed emission inventories and dispersion modeling assess pollutant concentrations (PM10, PM2.5, SO2, NOx) under various policy scenarios for 2025 and 2030. Business-as-usual (BAU) scenarios reveal the inadequacy of current regulations for PM, SO2, and NOx control. While BS-VI standards effectively reduced NOx emissions from vehicles, overall trends indicate a diminishing atmospheric capacity to assimilate pollutants. Alternative scenarios (ALTs) incorporating sector-specific controls project emission reductions (12–15 % PM10, 19–24 % PM2.5, 24–48 % SO2, 19–32 % NOx) and are expected to maintain pollutant levels below national ambient air quality standards. This integrated approach offers valuable insights for policymakers to develop sustainable air quality strategies aligned with SDGs. The study emphasises the need to consider potential trade-offs between environmental benefits and resource demands, such as increased electricity demand for electric vehicles (SDG 7) and land use competition from urban greening (SDG 11). This study aids in identifying strategies that maximise environmental benefits while minimising trade-offs, fostering sustainable urban development. The research's novelty lies in its integration of scenario-based analysis with atmospheric capacity evaluation, providing a robust framework for air quality management in rapidly urbanising regions.

Fuzzy logic-based estimation model of distribution transformers aging

Currently, we are moving towards more ecological modes of transport. As a result, the demand for electric vehicles is growing exponentially, and new loads, related to charging, can negatively affect the operation of electrical grids and their elements. Concurrent and uncontrolled charging of electric vehicles can cause a blackout. Considering the stochastic nature of the additional load from electric cars, it is difficult to predict such a load by analytical methods. The fuzzy logic approach can be used to formalize the tasks with ambiguity. In this paper, a fuzzy-based block diagram of the algorithm was developed in the MATLAB-Simulink environment for evaluating the impact of the charging load of electric vehicles on the aging of transformers, as well as a model was built and the effect of various penetrations of electric cars was evaluated. The fuzzy logic-based model includes the effects of ambient temperature, power quality, and overloads. It contains a diagnostic part that warns the dispatcher of the power distribution network about possible problems, providing up-to-date information about the state of the transformer. In addition, the efficiency of photovoltaic generators to increase the service life of distribution transformers was analyzed. The results show that photovoltaic power plants are not effective enough for high levels of electric vehicle penetration, and more advanced strategies should be used.

Polymer-Supported Graphene Sheet as a Vertically Conductive Anode of Lithium-Ion Battery.

The increasing demand for electric vehicles necessitates the development of cost-effective, mass-producible, long-lasting, and highly conductive batteries. Making this kind of battery is exceedingly tricky. This study introduces an innovative fabrication technique utilizing a laser-induced graphene (LIG) approach on commercial Kapton film to create hexagonal pores. These pores form vertical conduction paths for electron and ion transportation during lithiation and delithiation, significantly enhancing conductivity. The nongraphitized portion of the Kapton film makes it a binder-less, free-standing electrode, providing mechanical stability. Various analytical techniques, including scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), Raman spectroscopy, and atomic force microscopy (AFM) are utilized to confirm the transformation of a 3D porous graphene sheet from a commercial Kapton film. Cross-sectional SEM images verify the vertical connections. The specific capacity of 581 mAh g-1 is maintained until the end, with 99% coulombic efficiency at 0.1C. This simple manufacturing method paves the pathway for future LIG-based, cost-effective, lightweight, mass-producible, long-lasting, vertically conductive electrodes for lithium-ion batteries.

A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts

The aim of the study is the development of methodology for accurate estimation of electric vehicle demand; which is paramount regarding various aspects of the firms decision-making such as optimal price, production level, and corresponding amounts of capital and labor; as well as supply chain, inventory control, capital financing, and operational expenses management. The forecasting methods utilized include statistical techniques (autoregressive integrated moving average [ARIMA], and polynomial regression), machine learning (nonlinear autoregressive neural network [NAR]), deep learning (long short-term memory [LSTM]), hybrid and combination forecasting. With regard to the latter method, our study experiments with four different combining model approaches, including the introduction of an original, novel combining method with the employment of a transcendental LASSO function, which is used to form combinations of forecasts generated by the NAR, ARIMA, and polynomial regression models. The LASSO-based combining model proved superior to all other models, for the majority of forecast error statistics; where the root mean square error (RMSE) and mean absolute percentage error (MAPE) values are 4.5% and 8% respectively lower than the average level of the component model forecasts. The major implications of our empirical findings are that greater accuracy in demand forecasting can be achieved with a combining model approach, rather than reliance on any particular, singular model. Furthermore, given its superior performance, the employment of the studys LASSO-based combining model to forecast electric vehicle demand may lead to optimal firm decision-making over a range of organizational facets, which is predicated on accurate demand function estimation.

Application of improved Wolf pack algorithm in planning and operation of multi-microgrid systems with electric vehicles

Abstract With the rapid growth of renewable energy sources and the widespread use of electric vehicles (EVs), the planning and operation problems of multiple microgrids (MMGs) have become more complex and diverse. This paper develop an MMG model with multiple renewable energy sources and small-scale EVs, aiming to maximize the use of renewable energy sources and realize the charging demand of EVs, and highlighting the potential role of EVs in MMGs. In addition, the paper underscores the indispensable role of measurement technology in microgrids and the impetus that microgrid development provides for advancements in measurement technology. To this end, this paper proposes an improved Wolf pack algorithm (IWPA) based on the standard Wolf Pack Algorithm (WPA) with a spiral search approach and chaotic updating of individuals to improve the global search capability of the algorithm and the complexity of solving the scheduling problem. Through simulation experiments on ten standard test functions and examples, it is verified that the IWPA algorithm improves the search accuracy by 2.8%–6.8% and 13.9%–18.3% in the worst and best cases, respectively, in comparison with other algorithms, and it also has a faster convergence speed. Meanwhile, this paper proposes a load interval pricing strategy for the shortcomings of time-of-use pricing strategy and traditional real-time pricing strategy, which is simulated under grid-connected operation, isolated grid operation, and multi-microgrid cooperative operation modes, and the simulation results of the arithmetic example show that this strategy can effectively reduce carbon emissions, and IWPA can effectively coordinate renewable energy, EVs, and other energy resources to achieve efficient energy management of MMGs and supply-demand balance.

EV charging fairness protective management against charging demand uncertainty for a new “1 to N” automatic charging pile

Electric vehicles (EVs) have been popularly adopted and deployed over the past few years. However, the mismatch between EVs and charging infrastructure has become one of the major roadblocks to restricting EV promotion. Target at improve the temporal and spatial utilization rate of charging infrastructure, this paper presents a new “1 to N” automatic charging system with the combination of charging pile and special robotic arm. The connection between the charging pile and arrived EVs can be automatically switched by the robotic arm and the charging demand of EVs parking at the random parking spots could be satisfied. Based on the “1 to N” charging scenario a two-layer iterative charging scheduling strategy is proposed benefits of restraining the impact of the charging demand uncertainty while realizing the fairness in charging behavior. In addition, both the simulations and hardware in the loop experiments are implemented to verify the feasibility and real-time performance of the proposed “1 to N” charging system and charging schedule strategy.

Meeting the charging demand of Electric Vehicles in Greece: Enabling intercity trips

The increasing need for sustainable transportation has underscored the pivotal role of electric mobility in shaping the future of mobility. Electric Vehicles (EVs) have emerged as a promising solution, but their widespread adoption is contingent upon the availability of a reliable charging network. This challenge extends even to EV adoption pioneers like Norway and China. This study delves into the context of EV adoption in Greece, providing insights into the EV market, existing incentives, and the state of charging infrastructure. Reports indicate that Greece is at a critical juncture, with fast EV adoption but sluggish development of Charging Stations (CSs). To address this issue, a Monte Carlo simulation is employed to evaluate the feasibility of long-distance EV trips in Greece and propose an infrastructure development plan. For EVs, the study reveals a linear relationship between battery size and autonomy, with some exceptions emphasizing the importance of data acquisition and individual EV assessment. The study also provides insights into the relationship between trip length and energy consumption, indicating that longer trips exhibit greater fluctuation in energy consumption due to varying conditions, while energy usage patterns of shorter trips are more predictable. Additionally, the analysis presents average energy consumption values for each EV model in different temperature conditions, highlighting the impact of environmental factors on EV performance.

Mapping the charging demand for electric vehicles in 2050 from mobility habits

This paper proposes a method to spatially model and compare charging needs on the European scale, considering local disparities in population density, distance to city centres, car ownership and mobility habits. Mobility habits are modelled across Europe in terms of distance and time frame to elaborate scenarios of charging behaviour. The first step of the method is to calculate the density of electric vehicles with a resolution of 1km2, according to the progressive electrification of the fleet each year between 2020 and 2050. The second step is to quantify the mobility of commuters using their driving distance to work areas and mobility statistics. The model is then applied in a case study in Switzerland to plan the public charging infrastructure required to satisfy the charging needs of the local population. Despite lower motorization rates and driving distances, the results show a stronger need for charging in cities. With 50% of commuters charging at work and 20% at home during the workday, the demand in the evening can be reduced by 50% in the suburban areas compared to the baseline scenario in which all commuters are charging at home in the evening. This model can be used to quantify the energy needs of commuters, plan the deployment of the charging infrastructure, or simulate the effect of policies.

NdFeB Magnets Recycling via High-Pressure Selective Leaching and the Impurities Behaviors

Global concerns about climate change are driving increased demand of electric vehicles for sustainable transportation and turbines in emerging energy solutions, where permanent magnets (PMs) and rare earth elements (REEs) play a critical role. However, global REEs recycling rates are only 3% and 8% for light and heavy REEs, respectively. This work proposes an effective approach to separate the REEs and iron via high-pressure selective leaching by low-concentrated nitric acid from the end-of-life NdFeB magnet and investigates the impurities behavior during the leaching and precipitation steps. The results from the optimized leaching conditions demonstrated over 95% REEs leaching efficiency with less than 0.3% Fe dissolution. Approximately 70% of Al and B were leached as well, while other elements (Co, Ni, Cu) had leaching efficiencies below 40%, leaving a hematite rich residue. Adjusting the pH removes Al and Fe in leachate but minimally affects Cu, Co, and Ni. Na2S addition is more effective against transition metals, but both methods result in around 10% REEs loss. Direct oxalate precipitation is suggested for the obtained leachate, which can yield over 97.5% REEs oxides with approximately 1.0% alumina, which is acceptable for magnet remanufacturing due to the aluminum content commonly found in magnets. The technology developed in this study offers opportunities for closed-loop recycling and remanufacturing of PMs, benefiting the environment, economy, and supply chain security.Graphical