Electric Vehicles Research Articles

Functionalized Separators Boosting Electrochemical Performances for Lithium Batteries.

The growing demands for energy storage systems, electric vehicles, and portable electronics have significantly pushed forward the need for safe and reliable lithium batteries. It is essential to design functional separators with improved mechanical and electrochemical characteristics. This review covers the improved mechanical and electrochemical performances as well as the advancements made in the design of separators utilizing a variety of techniques. In terms of electrolyte wettability and adhesion of the coating materials, we provide an overview of the current status of research on coated separators, in situ modified separators, and grafting modified separators, and elaborate additional performance parameters of interest. The characteristics of inorganics coated separators, organic framework coated separators and inorganic-organic coated separators from different fabrication methods are compared. Future directions regarding new modified materials, manufacturing process, quantitative analysis of adhesion and so on are proposed toward next-generation advanced lithium batteries.

Correction: A CryStAl-RDF technique-based integrated circuit topology for fast charging station of electric vehicle (EV)

Correction: A CryStAl-RDF technique-based integrated circuit topology for fast charging station of electric vehicle (EV)

Analyzing Organic Electrolyte Solvents from Spent Lithium-Ion Batteries as a Basis for Distillative Value Component Recovery

The rapid expansion of lithium-ion batteries (LIBs), largely driven by the rising demand for electric vehicles, will lead to a significant increase in end-of-life (EOL) batteries, necessitating efficient recycling processes, which must be accompanied by equally efficient purification steps. This study addresses the challenge of reusing organic electrolyte solvents from spent LIBs, a key component often overlooked in existing recycling strategies. To address this issue, we developed a gas chromatography (GC) method. A variety of spent electrolyte samples of different origin, including mechanical-thermal pretreatment or direct cell recovery, were analyzed by quantification of common solvents and identified organic impurities. Results demonstrated that the composition of the recovered electrolytes was highly variable, with concentrations fluctuating. Impurities were identified, which may originate from various sources throughout the lifespan of an LIB and have the potential to reduce the performance of second-life LIBs by reusing the electrolyte without any purification. The findings highlight the necessity for advanced purification methods like a distillation process to remove these impurities and ensure the viability of recycled electrolytes in maintaining the performance and safety standards required for LIBs. This research contributes to the broader goal of enhancing the sustainability and reuse of battery materials.

Nonlinear Impact Analysis of Urban Road Traffic Carbon Emissions Based on the Integration of Gasoline and Electric Vehicles

With the rapid proliferation of electric vehicles (EVs) in China, the landscape of transportation carbon emissions has undergone significant changes. However, research on the impact of the built environment on the carbon emissions of mixed traffic from gasoline and electric vehicles remains sparse. This paper focuses on urban traffic scenarios with a mix of gasoline and electric vehicles, analyzing the spatiotemporal distribution of carbon emissions from both types of vehicles and their nonlinear association with the built environment. Utilizing trajectory data from gasoline-powered and electric taxis in Chengdu, China, we establish segment-level carbon emission estimation models based on the vehicle-specific power of gasoline vehicles and the equivalent energy consumption of electric vehicles. Subsequently, we employ the XGBoost algorithm and SHapley Additive ExPlanation (SHAP) to analyze the nonlinear relationships between 13 built environment variables and vehicle carbon emissions. This paper reveals that most built environment variables exhibit nonlinear relationships with traffic carbon emissions, with five factors—population density, road density, residential density, metro accessibility, and the number of parking lots—having a significant impact on road carbon emissions. Finally, we discuss the carbon reduction benefits of EV adoption and propose policy recommendations for low-carbon initiatives in the transportation field.

Hybrid intelligent optimization strategy of battery swapping station for electric vehicles based on reinforcement learning

Abstract Smart transportation is an important application scenario in the field of urban computing. As the popularity of electric vehicles increases, the demand for fast charging is growing rapidly. In response to this, battery swapping stations are being proposed as a solution, but their operational efficiency is challenged by factors such as battery life, vehicle queues, and grid load management. In this paper, a mixed intelligent optimization strategy combining the proximal policy optimization (PPO) algorithm from reinforcement learning and the goat swarm optimization (GSO) algorithm is proposed. The GSO-PPO algorithm is constructed, where PPO algorithm learns the optimal scheduling strategy for the battery swapping station in a dynamic environment, and the GSO algorithm optimizes the hyperparameters of PPO and adjusts the weight of the reward function to achieve the multi-objective optimization of minimizing battery life, shortening vehicle waiting time, and efficiently managing grid load. The experimental results show that compared with random strategies and traditional PPO algorithms, GSO-PPO reduces vehicle waiting time and improves service efficiency, making the overall operation of the battery swapping station more stable. The study demonstrates the potential of combining reinforcement learning and swarm intelligence algorithms in smart energy infrastructure and solving multi-objective optimization problems.

Sustainable hybrid systems for electric vehicle charging infrastructures in regional applications.

Increasing greenhouse gas (GHG) emissions and environmental issues have heightened the demand for renewable energy sources (RES) and prompted a swift transition to electric vehicles (EVs) in the transportation sector. This shift underscores the need to address the challenges of electricity supply and continuity for electric vehicle charging stations (EVCS). This study aims to determine the most suitable hybrid systems to ensure the electricity supply to EVCSs in the Çukurova region of Adana, Turkey. Six different scenarios involving components such as photovoltaic (PV) panel, wind turbine (WT), biomass generators (BG), electrolyzer (Elz), hydrogen tank (HT), fuel cell (FC), batteries (Bat), inverter (Inv), and the grid were analyzed using HOMER Pro microgrid analysis tool version 3.14.2 software. The optimization results indicated that the most feasible system was Scenario 4, comprising the PV, BG, Elz, HT, FC, Inv, and grid components. This scenario's total net present cost (NPC) was $611,283.50, with a levelized cost of energy (LCOE) of $0.0215. The annual energy production and consumption were 1,507,169 kWh and 1,420,714 kWh, respectively. The fact that the energy generated from exceeds the energy sourced from the grid reduces the payback period of the system. These findings highlight the economic and sustainable potential of renewable hybrid systems for enhancing the performance of EVCS in solar-rich regions.

Energy storage management in electric vehicles

Energy storage management in electric vehicles

Framework for Investment in Electricity Distribution to Enable Energy Transition

The climate crisis, caused by the emission of greenhouse gases, requires urgent attention and the implementation of effective measures to mitigate it. Within this scenario, the energy transition, a shift from an energy matrix based on fossil fuels to one with low or zero carbon emissions, emerges as a fundamental strategy for neutralizing these emissions. Given this context, the main objective of this study is to propose and validate an investment model in distribution infrastructure to enable the integral and effective development of the energy transition in Brazil. To this end, a systematic review was conducted, which provided the necessary foundation for elaborating a model aimed at estimating the investment required in the distribution of electricity in Brazil, based on the variables identified in the literature. Through the Delphi method, this model was validated by experts. From this perspective, electric vehicles, distributed generation, energy storage, and demand response are key elements in this process. Given this scenario, the electric sector, particularly the distribution of electric power, plays a crucial role in enabling the energy transition.

Synthetic Data Generation for AI-Informed End-of-Line Testing for Lithium-Ion Battery Production

Lithium-ion batteries are a key technology in supply chains for modern electric vehicles. Their production is complex and can be prone to defects. As such, the detection of defective batteries is critical to ensure performance and consumer safety. Existing end-of-line testing relies heavily on electrical measurements for identifying defective cells. However, it is possible that not all pertinent information is encoded within the electrical measurements alone. Reversible expansion in lithium-ion cells is an indicator of lithiation within the cell, while irreversible expansion is a consequence of the ageing process; unexpected expansion may indicate the presence of undesirable defects. By measuring expansion in addition to electrical measurements, we aim to make better and faster quality predictions during end-of-line testing, thereby facilitating the early detection of potential defects. To make these predictions, we implement artificial intelligence algorithms to extract information from the measurements. Training these networks requires large training datasets, which are expensive to produce. In this paper, we demonstrate a first-order physical modelling approach for generating synthetic data to pre-train artificial intelligence algorithms that perform anomaly detection on lithium-ion battery cells at the end-of-line. The equivalent circuit model used to generate voltage curves could be fit to real data with a mean absolute error of less than 1%, and the expansion model could be fit to a mean absolute error of less than 2% of the measured values. By pretraining the artificial intelligence network using synthetic data, we can leverage existing physical models to reduce the amount of training data required.

Comparative Analysis of BTM Systems Made of a Fireproof Composite Material with Nano Boron Nitride

The paper presents a numerical analysis of the possibilities of replacing the aluminum serpentines in the current construction of battery thermal management systems (BTMS) with cooling serpentines made of fireproof composite materials with high heat transfer parameters (fireproof epoxy resin + nano boron nitride). This approach was given by the need to replace aluminum (which, in case of fire, maintains and accelerates the combustion process) with fireproof materials that reduce/eliminate the fire risk due to improper battery operation. Numerical analysis methods were used through simulation to identify the most efficient design among the single-channel, multichannel, multiflow and multiple coolant inlet–outlet solutions for cooling serpentine. In addition to these geometric constructive parameters, the variation of the coolant flow rate (9, 12, 15 and 18 L/min) and coolant inlet temperature (17, 20 and 25 °C) was also considered. The obtained results showed that the single-inlet nanocomposite resin cooling serpentine four-channel configuration presents the highest cooling efficiency of the cells that form the battery module while ensuring very good thermal uniformity as well. These findings are supported by the lowest average heat absorption by the batteries, of 34.44 kJ, as well as the lowest average internal resistance difference (caused by thermal gradients), of 5.23%. Future research is needed to identify the degree of structural resistance of serpentines made of fireproof composite material to external stresses (vibrations characteristic of the operation of electric vehicles).

Research on Yaw Stability Control of Distributed Drive Electric Vehicle Based on Super-twisting Sliding Mode Algorithm

Research on Yaw Stability Control of Distributed Drive Electric Vehicle Based on Super-twisting Sliding Mode Algorithm

Enhancing Electric Vehicle Autonomy With Solar Energy: A Case Study of the “Takai Urban” in Northern Chile

The global transition towards electromobility presents unique challenges, particularly in regions with limited infrastructure. In Northern Chile, the sparse distribution of charging stations, with distances often exceeding 100 km, complicates the widespread adoption of electric vehicles (EVs). This study investigates the feasibility of enhancing the autonomy of the "Takai Urban", a solar-assisted battery EV through the integration of Zebra IBC solar cells, leveraging the region's high solar irradiance. Initially, the Takai urban EV had a maximum range of 70 km per charge. Through virtual modelling and simulations, which incorporated GPS and meteorological data along a typical 43.3 km route in Antofagasta city, we examined the impact of solar integration on vehicle performance. The simulations indicated that with the strategic placement of high-efficiency solar cells, the vehicle's range could be extended by approximately 48%, achieving a new maximum of 104 km per charge. This approach not only demonstrates the potential for solar-assisted EVs in regions like Northern Chile but also underscores the broader implications for sustainable mobility in similar environments globally. The results highlight the potential for practical enhancements in EV designs using solar technologies, focusing on efficiency improvements and sustainable mobility solutions.

Time-Domain Aggregation of Interharmonics from Parallel Operation of Multiple Sustainable Sources and Electric Vehicles

This paper examines the random nature of interharmonics generated by power converters connected to sustainable energy sources and loads, such as wind turbines, photovoltaic (PV) panels, and electric vehicles (EVs). Current research often overlooks the stochastic behavior of interharmonics and their impact on power system reliability and resilience, leading to gaps in effective modeling and mitigation strategies. Thus, this study examines a low-voltage installation with a PV panel, an EV and a microwave operating simultaneously, providing practical insights into real-world scenarios of interharmonic related disruptions and solutions for enhancing the reliability and resilience of sustainable energy grids. By leveraging real-time measurements of interharmonics, suitable probability distribution functions (PDFs) are initialized to develop a probabilistic model using Monte Carlo simulation. This enables the derivation of a time-domain aggregation model of interharmonics from multiple sources operating together at the point of common coupling (PCC). The findings reveal that the peak values of voltage or current fluctuations at the PCC are influenced by the randomness in the number of devices connected and the frequency components originating from different sources. Through multiple case studies, the dependency of these fluctuations on stochastic parameters is systematically established. Empirical relationships are formulated to predict aggregated interharmonic values under varying scenarios, enhancing the accuracy and applicability of the model. The results demonstrate that higher interharmonic frequencies and fewer randomly connected devices significantly increase the probability of elevated aggregated peak values. These insights can serve as benchmarks for grid operators and policymakers in mitigating interharmonic related issues in modern power systems.

Electric Vehicle and Soft Open Points Co-Planning for Active Distribution Grid Flexibility Enhancement

With the increasing penetration of distributed generation (DG), the supply–demand imbalance and voltage overruns in the distribution network have intensified, and there is an urgent need to introduce flexibility resources for regulation. This paper proposes co-planning of electric vehicles (EVs) and soft opening points (SOPs) to improve the flexibility of the active distribution network, thereby improving the economics and flexibility of the distribution network. Firstly, this paper establishes a charging pile day-ahead dispatchable prediction model and a real-time dispatchable potential assessment model through Monte Carlo sampling simulation. It replaces the traditional energy storage model with this model and then solves the EV and SOP collaborative planning model using a second-order conical planning algorithm with the objective function of minimizing the annual integrated cost. At the same time, the flexibility of the distribution network is analyzed by two indicators: power supply and demand balance and branch load margin. Finally, the optimization method proposed in this paper is analyzed and validated on an improved IEEE 33-node distribution system. Example results show that the planning method proposed in this paper can effectively reduce the annual comprehensive operating cost of distribution networks, meet the flexibility index, and be conducive to improving the economy and flexibility of distribution network operation.

Optimizing electric vehicle range through hybrid SHO-HDNN technique

Optimizing electric vehicle range through hybrid SHO-HDNN technique

Study on the Extraction of Rare Earth Elements (REEs) from Phosphogypsum Using Gluconobacter Oxydans Culture Solution

With the rapid development of modern industry, particularly in the fields of electric vehicles and renewable energy technologies, the demand for rare earth elements (REEs) has surged dramatically. Phosphogypsum (PG), which is an industrial waste product generated during the production of phosphoric acid through the sulfuric acid process, is rich in REEs. However, traditional chemical leaching methods pose environmental pollution and resource wastage issues. This study aims to explore the feasibility and optimal conditions for bioleaching REEs from phosphogypsum using Gluconobacter oxydans (G. oxydans). The phase composition and components of phosphogypsum, as well as the growth characteristics and leaching efficiency of G. oxydans, were analyzed in detail using SEM, EDS, XRD, and XRF techniques. Experimental results indicate that G. oxydans can effectively leach REEs from phosphogypsum under conditions of 28 °C, an agitation speed of 150 rpm, and a liquid-to-solid ratio of 4:1, with a maximum leaching efficiency of 24.67%. Moreover, it is revealed in the study that G. oxydans exhibits selectivity in leaching REEs. Specifically, the leaching efficiency for Nd is significantly enhanced at low pH values. This research provides a theoretical basis and practical application example for the efficient and environmentally friendly recovery of REEs from phosphogypsum.

EVCS Demand Management using Multi-Objective Power Flow Analysis with Renewable Distributed Generators

Abstract The optimal placement and sizing of multiple distributed generator units in strategic locations are essential for effective energy management of Electric vehicle charging station (EVCS) in modern power systems. This study aims to minimize power losses, optimize Renewable based Distributed Generator (RDG) sizing, and reduce fuel costs of conventional generators while also considering the impact on voltage stability by using multi objective Newton-Raphson (NR) power flow method. Two meta-heuristic multi-objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Differential Evolution (MODE) techniques have been merged with NR power flow analysis. In this paper two scenarios are analyzed: the base case under normal conditions and with EVCS where the load is increased by 1.1 times the base load. The results demonstrate that the MOGWO approach outperforms the MODE method, yielding more favorable outcomes in both scenarios. Notably, the MOGWO method results in smaller RDG sizes compared to MODE in both normal and EVCS loading conditions. Transmission losses are reduced by 68.12% under normal conditions and by 72.7% under EVCS loading conditions with the MOGWO approach. The effectiveness of the proposed method has been evaluated using MATLAB software on the standard IEEE 57-bus system. Additionally, the base case results have been compared with other algorithms, including SCA, DA, NSGA-II, and MOCS, to validate the performance of the proposed MOGWO approach.

Electrochemical Direct Lithium Extraction: A Review of Electrodialysis and Capacitive Deionization Technologies

The rapid expansion of lithium-ion battery (LIB) markets for electric vehicles and renewable energy storage has exponentially increased lithium demand, driving research into sustainable extraction methods. Traditional lithium recovery from brine using evaporation ponds is resource intensive, consuming vast amounts of water and causing severe environmental issues. In response, Direct Lithium Extraction (DLE) technologies have emerged as more efficient, eco-friendly alternatives. This review explores two promising electrochemical DLE methods: Electrodialysis (ED) and Capacitive Deionization (CDI). ED employs ion-exchange membranes (IEMs), such as cation exchange membranes, to selectively transport lithium ions from sources like brine and seawater and achieves high recovery rates. IEMs utilize chemical and structural properties to enhance the selectivity of Li+ over competing ions like Mg2+ and Na+. However, ED faces challenges such as high energy consumption, membrane fouling, and reduced efficiency in ion-rich solutions. CDI uses electrostatic forces to adsorb lithium ions onto electrodes, offering low energy consumption and adaptability to varying lithium concentrations. Advanced variants, such as Membrane Capacitive Deionization (MCDI) and Flow Capacitive Deionization (FCDI), enhance ion selectivity and enable continuous operation. MCDI incorporates IEMs to reduce co-ion interference effects, while FCDI utilizes liquid electrodes to enhance scalability and operational flexibility. Advancements in electrode materials remain crucial to enhance selectivity and efficiency. Validating these methods at the pilot scale is crucial for assessing performance, scalability, and economic feasibility under real-world conditions. Future research should focus on reducing operational costs, developing more durable and selective electrodes, and creating integrated systems to enhance overall efficiency. By addressing these challenges, DLE technologies can provide sustainable solutions for lithium resource management, minimize environmental impact, and support a low-carbon future.

Wide output voltage range LLC resonant converter for 800 V electric vehicles battery chargers

Wide output voltage range LLC resonant converter for 800 V electric vehicles battery chargers

Assessing Factors Impacting Electric Vehicle Adoption in Saudi Arabia: Insights on Willingness to Pay, Environmental Awareness, and Perceived Risk

As Saudi Arabia seeks to transition toward sustainable energy, the adoption of Electric Vehicles (EVs) is a key component in reducing carbon emissions and combating climate change. This study explores the factors driving EV adoption, focusing on Willingness To Pay (WTP), Environmental Awareness (EA), Perceived Risks (PR), and Product Attributes (PA). Using a structured survey distributed to 365 respondents, the obtained data were analyzed through the SPSS 27 software, employing regression analysis and factor analysis. The results reveal that WTP and EA are significant predictors of Perceived Value (PV), which, in turn, positively influences consumers’ intention to purchase EVs. Conversely, PR negatively impacts Purchase Intention (PI), though these risks are mitigated by favorable PA. The findings highlight a gap between consumer interest in EVs and the existing infrastructure, suggesting that addressing these concerns is crucial for widespread EV adoption in Saudi Arabia. These insights provide actionable recommendations for policymakers and businesses aiming to enhance consumer confidence and facilitate the growth of the EV market in the region.