Electric Vehicles Research Articles

Enhanced Wavelet Transform Dynamic Attention Transformer Model for Recycled Lithium-Ion Battery Anomaly Detection

Rapid advancements in electric vehicle (EV) technology have highlighted the importance of lithium-ion (Li) batteries. These batteries are essential for safety and reliability. Battery data show non-stationarity and complex dynamics, presenting challenges for current monitoring and prediction methods. These methods often fail to manage the variability seen in real-world environments. To address these challenges, we propose a Transformer model with a wavelet transform dynamic attention mechanism (WADT). The dynamic attention mechanism uses wavelet transform. It focuses adaptively on the most informative parts of the battery data to enhance the anomaly detection accuracy. We also developed a deep learning model with an improved Transformer architecture. This architecture is tailored for the complex dynamics of battery data time series. The model accounts for temporal dependencies and adapts to non-stationary behavior. Experiments on public battery datasets show our approach’s effectiveness. Our model significantly outperforms existing technologies with an accuracy of 0.89 and an AUC score of 0.88. These results validate our method’s innovation and effectiveness.

Microstructure-based digital twin thermo-electrochemical modeling of LIBs at the cell-to-module scale

As the application of lithium-ion batteries (LIBs) expands beyond conventional electric vehicles (EVs) to heavy vehicles such as electric trucks or trams, the importance of thermal management in LIB systems is increasing, even at the module or pack level. In particular, because monitoring the thermal behaviors of each cell is not feasible, thermo-electrochemical modeling and simulations in the module or pack level are essential for analyzing and ensuring thermal stability. However, because the conventional lumped thermo-electrochemical models cannot reflect the actual structure of LIB cells, there might be considerable differences may exist between simulation and experimental results. To fill these gaps, we have newly developed a 3D microstructure-based digital twin model of a battery module (8.8 Ah/18.5 V, five LIB pouch cells in series) for an unmanned railway vehicle. Unlike traditional lumped models, our digital twin model accurately well reflects the internal structure of cells and can calculate the heat generation of each component inside a cell. As a result, contrary to a lumped model, the digital twin model can not only simulate the inhomogeneous temperature gradient inside a cell, but also estimates higher local maximum temperatures (TDT, max/TL, max = 137.2 °C/123.9 °C @ 10C discharge) in cells which can trigger thermal runaway. Therefore, microstructure-based digital twin modeling can alleviate concerns regarding the thermal runaway of LIB cells, modules, and packs, and provide safe operating conditions.

Universal Soft Switching Dual Active Bridge Converter for Electric Vehicle Charging Infrastructure towards Sustainable Electric Transportation

This paper introduces a modified Dual Active Bridge (DAB) converter integrated with an LLC2 resonant converter tailored for Electric Vehicle (EV) charging infrastructure, emphasizing a unidirectional power flow. The converter implements a two-stage power conversion process, leveraging the DAB’s suitability for high-power applications characterized by enhanced efficiency, low voltage stress, and universal soft switching (ZVS) across all switches. Distinguishing itself from conventional isolated and non-isolated DC-DC converters, the DAB-LLC2 configuration is systematically elucidated in terms of its operating principle, soft switching characteristics, steady-state attributes, output features and crucial design parameters. A meticulous design of a 3kW DAB-LLC2 converter is conducted using MATLAB/Simulink software and the comprehensive results are documented, providing valuable insights into the converter’s performance and applicability for EV charging infrastructure for sustainable electric transportation.

Unlocking sustainable lithium: A comparative life cycle assessment of innovative extraction methods from brine

The surging demand for lithium, driven by the widespread adoption of electric vehicles and renewable energy storage systems, underscores the urgent need to develop sustainable lithium extraction methods. This study presents a comprehensive Life Cycle Assessment Using the TRACI method to evaluate and compare the environmental impacts of solvent extraction, adsorption, nanofiltration, and membrane electrolysis as direct lithium extraction methods for recovering lithium from brine to produce lithium carbonate. In terms of global warming, the carbon dioxide emission for each process was determined as follows: solvent extraction emits 52.7 kg CO2eq/kg of lithium carbonate, adsorption emits 47.9 CO2eq/kg of lithium carbonate, nanofiltration emits 17.7 kg CO2eq/kg of lithium carbonate, and membrane electrolysis emits 80.57 kg CO2eq/kg of lithium carbonate. As a result, the nanofiltration process emerges as the most environmentally friendly method, offering a promising solution to the environmental challenges of lithium extraction. In contrast, the membrane electrolysis process has the highest environmental impact.

Skewness impact through distributional evaluation (SITDE) method: a new method in multi-criteria decision making

In this article, we propose the Skewness Impact Through Distributional Evaluation (SITDE) method for multi-criteria decision-making. Unlike traditional methods ignoring criteria distribution asymmetry, SITDE enhances the decision quality. We use a case-study on electric vehicle selection to illustrate the efficiency of SITDE relative to conventional methods. The comprehensive decision support with SITDE across multiple thresholds is an important aspect of a detailed and balanced decision-making virtue as our results show in case the skewness effects are included. Theoretical backgrounds, typologies practical applications and statistical justifications to confirm the accuracy of SITDE in regarding to limit complexities MCDM scenarios are explained this paper. SITDE enhances a robust decision-making process by accounting for the inherent biases of symmetric assumption models. Moreover, SITDE proves to be extremely useful in cases for dealing with asymmetrical data distributions and skewed datasets humanitarian cause as infected situations are always overwhelming. This broad applicability indicates the potential for wider use with respect to different domains, such as in environmental management and financial risk assessment. Future studies should investigate the adequacy or insufficiency of its fusion with different MCDM methods and explore if this approach would apply a more efficient and valid insight that might enhance decision making in multiple sectors.

Fuzzy Adaptive State Estimation of Distributed Drive Electric Vehicles with Random Missing Measurements and Unknown Process Noise

Accurate estimation of sideslip angle and vehicle velocity is crucial for effective control of distributed drive electric vehicles. However, as these states are not directly measured, Kalman-based approaches utilizing in-vehicle sensors have been developed to estimate them. Unfortunately, existing methods tend to ignore the impact of data loss on estimation performance. Furthermore, the process noise, which changes dynamically due to varying driving conditions, is not adequately considered. In response to these constraints, we propose a novel method called the fuzzy adaptive fault-tolerant extended Kalman filter (FAFTEKF). Initially, a fault-tolerant EKF is devised to handle missing measurements. Additionally, a fuzzy logic system that dynamically updates the process noise matrix, is built to improve estimation accuracy under different driving conditions. Extensive experimental results validate the superiority of the FAFTEKF over the traditional EKF across various scenarios with different degrees of data loss.

In‐use dissipation of technology‐critical elements from vehicles and renewable energy technologies in Vienna, Austria: A public health matter?

AbstractThe rollout of electric vehicles and photovoltaic panels is essential to mitigate climate change. However, they depend on technology‐critical elements (TCEs), which can be harmful to human health and whose use is rapidly expanding, while recycling is lacking. While mining has received substantial attention, in‐use dissipation in urban areas has so far not been assessed, for example, corrosion and abrasion of vehicle components and weather‐related effects affecting thin‐film photovoltaic panels. Therefore, the question arises to which extent TCEs dissipate during use and which potential non‐occupational human health impacts could occur. We assessed the available information on urban in‐use dissipation and human health concerns and conducted exploratory modeling of in‐use technology stocks, in‐ and outflows, and in‐use dissipation of neodymium, dysprosium, lanthanum, praseodymium, cerium, gallium, germanium, and tellurium contained in 21 vehicle and renewable energy technologies, for Vienna, Austria. In prospective scenarios, TCE dynamics in a trend‐continuation vis à vis official city policy plans and a more ambitious transition scenario were then assessed. We find that electrifying the vehicle fleet without demand‐reduction is the main driver of TCE consumption, effectively doubling cumulative end‐of‐life outflows to 3,073 [2,452–3,966] t and cumulative in‐use dissipation to 9.3 [5.2–15.7] t by the year 2060. Sufficiency‐based measures could reduce demand and in‐use dissipation well below levels with continued trends, thus highlighting the need to combine decarbonization with demand‐reducing measures. These results help assess potential future in‐use dissipation dynamics and inform discussions about potential public health hazards associated with exposure to TCEs accumulating in the urban environment.

Simultaneous accelerated stress testing of membrane electrode assembly components in polymer electrolyte fuel cells

The durability of polymer electrolyte fuel cells (PEFCs) in fuel cell electric vehicles is important for the shift from passenger cars to heavy-duty vehicles. The components of a PEFC, namely the proton exchange membrane (PEM), catalyst layer (CL), and gas diffusion layer (GDL), contribute to the degradation of the fuel cell performance. In this paper, we propose a method for simultaneously evaluating the degradation rates of these components by combining electrochemical characterization with operando synchrotron X-ray radiography. The open-circuit voltage, electrochemically active surface area (ECSA), and water saturation were used as the degradation indicators for the PEMs, CLs, and GDLs, respectively. The results of two accelerated stress tests (loading and start-stop cycles) after 10,000 cycles showed that the increase in water saturation owing to the loss of hydrophobicity due to carbon corrosion in the cathode GDL occurred on the same timescale as the degradation in the PEM and cathode CL. Specifically, during the load cycle AST, the cathode CL degraded with a 26% reduction in the ECSA along with the cathode GDL degradation with a 10% increase in water saturation. This suggests that more efforts should be devoted to studies on the durability of GDLs for heavy-duty applications.

Electric Vehicle Adoption: Implications for Employment in South Africa’s Automotive Component Industry

The transition to electric vehicles (EVs) will require significant changes in the automotive industry, particularly concerning its labour force. This study evaluates the impact of EVs on employment within South Africa’s automotive component manufacturing sector. A system dynamics model was developed to assess the effect of EV market penetration on component manufacturing employment over time. Key drivers of employment in the conventional and the EV component industries were identified and incorporated into the model. The results indicate a negative impact of EV penetration on employment of 18.3% when considering 20.0% EV sales (EV20) in 2040. Scenario analyses highlighted the influence of individual components, battery localisation, and load shedding on labour. Tyre and wheel manufacturing was found to be the most labour impactful component in the conventional industry against electrical engines in the EV counterpart. Localising 25.0% of battery production could increase employment by 6.9% and 2.7% in the EV40 and EV20 Scenarios. Load shedding has a detrimental effect on the country’s economy, assumed to reduce employment by 30.0%. However, strategic industry and policy interventions can mitigate the adverse effects of this transition.

El auge del coche eléctrico en China y su impacto en la Unión Europea

Rationale China’s electric vehicle sector has recently aroused considerable interest thanks to its rapid growth and current dominance of the global market. Given the importance of the automotive sector in Spain and the European Union (EU), and the recent introduction of tariffs, it is worth exploring the factors behind this rise. Takeaways •Battery electric vehicle (BEV) market penetration in the EU has grown notably in recent years: in 2023 BEVs accounted for 15% of all vehicles sold (5.4% in Spain), a figure well above the 1.9% recorded in 2019 (0.7% in Spain). •China has secured its status as the world’s leading electric vehicle manufacturer and exporter, its output proving particularly popular in the European market. As a consequence, the market share of BEVs made in China rose from 0.4% of new BEV registrations in 2019 to 20% in 2023. •The success of Chinese BEV exports rests on lower manufacturing costs, thanks largely to China’s control over the supply chain, government support policies at various stages of production and the country’s early embrace of electric vehicles.

EOS: Impact Evaluation of Electric Vehicle Adoption on Peak Load Shaving Using Agent-Based Modeling

The increasing adoption of electric vehicles (EVs) by the general population creates an opportunity to deploy the energy storage capability of EVs for performing peak energy shaving in their households and ultimately in their neighborhood grid during surging demand. However, the impact of the adoption rate in a neighborhood might be counterbalanced by the energy demand of EVs during off-peak hours. Therefore, achieving optimal peak energy shaving is a product of a sensitive balancing process that depends on the EV adoption rate. In this paper, we propose EOS, an agent-based simulation model, to represent independent household energy usage and estimate the real-time neighborhood energy consumption and peak shaving energy amount of a neighborhood. This study uses Residential Energy Consumption Survey (RECS) and the American Time Use Survey (ATUS) data to model realistic real-time household energy use. We evaluate the impact of the EV adoption rates of a neighborhood on performing energy peak shaving during sudden energy surges. Our findings reveal these trade-offs and, specifically, a reduction of up to 30% of the peak neighborhood energy usage for the optimal neighborhood EV adoption rate in a 1089 household neighborhood.

Optimization of vehicle conceptual design problems using an enhanced hunger games search algorithm

Abstract Electric vehicles have become a standard means of transportation in the last 10 years. This paper aims to formalize design optimization problems for electric vehicle components. It presents a tool conceptual design technique with a hunger games search optimizer that incorporates dynamic adversary-based learning and diversity leader (referred to as HGS-DOL-DIL) to overcome the local optimum trap and low convergence rate limitations of the Hunger Games search algorithm to improve the convergence rate. The performance of the proposed algorithms is studied on six widely used engineering design problems, complex constraints, and discrete variables. For the HGS-DOL-DIL practical feasibility analysis, a case study of shape optimization of an electric car suspension arm from the industry is carried out. Overall, the inclusion of the OL strategy has proven its superiority in solving real-world problems, especially in solving real-world problems such as shape optimization of an electric vehicle automobile suspension arm, showing that the algorithm improves the search space improves the solution quality, and reflects its potential to find global optimum solutions in a well-balanced exploration and exploitation phase.

Electric vehicle energy consumption prediction for unknown route types using deep neural networks by combining static and dynamic data

Accurate energy consumption prediction of electric vehicles (EVs) is crucial for drivers considering long trips. All the data should be provided beforehand to determine the energy consumption at the beginning of the trip. Although dynamic vehicle data (vehicle speed, state-of-charge, acceleration, etc.) cannot be known before the trip, factors related to the specified route (route type, elevation, average speed, weather, driving time, etc.) can be used to predict the consumed energy. These factors can be categorized as static and dynamic features, and thus, the question of how to effectively use static and dynamic data arises. This paper investigates the problem of predicting the energy consumption of an EV for a predetermined trip using a deep neural network (DNN) model that effectively uses static features along with dynamic segment features. Furthermore, we address the problem where the route types are unknown in advance. To include more information in the prediction model, we clustered the speed profiles using shape-based clustering with dynamic time warping (DTW) to predict the route type and used the cluster labels as static inputs. Real driving data collected from various drivers of a specific EV were used to train the DNN. The proposed DNN model was compared with the average energy consumption (AEC) model and five machine learning models. The results show that labels obtained from shape-based clustering improved the prediction more than feature-based cluster labels. The prediction errors were minimized with the proposed DNN model, where static features are introduced to the first and second layers twice.

Transition from Electric Vehicles to Energy Storage: Review on Targeted Lithium-Ion Battery Diagnostics

This paper examines the transition of lithium-ion batteries from electric vehicles (EVs) to energy storage systems (ESSs), with a focus on diagnosing their state of health (SOH) to ensure efficient and safe repurposing. It compares direct methods, model-based diagnostics, and data-driven techniques, evaluating their strengths and limitations for both EV and ESS applications. This study underscores the necessity of accurate SOH diagnostics to maximize battery reuse, promoting sustainability and circular economy objectives. By providing a comprehensive overview of the battery lifecycle—from manufacturing to recycling—this research offers strategies for effective lifecycle management and cost-effective, environmentally sustainable secondary battery applications. Key findings highlight the potential of second-life EV batteries in ESSs. The integration of the considered diagnostic methods was shown to extend battery lifespan by up to 30%, reduce waste, and optimize resource efficiency, which is crucial for achieving circular economy objectives. This paper’s insights are crucial for advancing sustainable energy systems and informing future research on improving diagnostic methods for evolving battery technologies.

A study on the determinants of consumer purchase intention on electric vehicles

The purpose of this research was to examine the variables that influence customer purchase intentions for electric vehicles (EVs), with a focus on electric cars in Malaysia. Four independent factors, namely social media, Perceived Usefulness, Subjective Norm, and Government Policy were examined to determine their impact on customer purchase intention. The study used a descriptive correlation design where respondents completing a questionnaire provided via Google Forms using a 5-point Likert scale. The Theory of Planned Behaviour (TPB) serves as the underpinning foundation for this research. The convenience sampling technique was used in collecting responses from 385 samples. The data collected is analysed using SPSS Version 29 including descriptive and inferential statistics. The findings seek to fill the knowledge gaps in Malaysian consumers' EV purchasing intentions and what are the influential variables. Findings provide policymakers and industry stakeholders with more information into the way to successfully plan and promote EV adoption in Malaysia's automotive sector.

Boron nitride: The key material in polymer composites for electromobility

AbstractDespite the continuous development and improvement of many technologies and multifunctional materials for the electric powertrain (ePowertrain) for electric vehicles, there are still technical issues and challenges to address such as thermal management in batteries, electric motors, and power electronic devices, as most of their failures are due to poor thermal management. Consequently, conventional engineering polymer materials already used must be replaced since most of them have low thermal conductivity and are therefore limited in performance for thermal management applications. A key solution is to develop highly thermally conductive polymer composites that combine other features, such as flame‐retardant, electrical insulation, and mechanical and barrier properties, by incorporating fillers into the polymer matrix. This approach has attracted intensive research efforts. In this review, we first examine the key drivers, trends, and solutions of the ePowertrain segment, emphasizing thermal management. Second, special attention is given to the state‐of‐the‐art boron nitride (BN) polymer composites with current or potential applications in the automotive industry, especially, in batteries, electric motors, and power electronics. Third, analysis and prediction of thermal properties of BN polymer composites by finite element simulation are presented. Finally, outlooks for future research in this field are highlighted.Highlights Thermal management of batteries, electric motors and power electronics, using BN polymer composites, optimizes the functionality of electric vehicles. Cross‐linked polymers with BNNSs provide resins for high power motors, film capacitors, and Li‐metal battery electrolytes for electric vehicles. Mathematical modeling and life cycle analysis can predict trends and research gaps in ePowertrain applications.

Conceptual model for extending electric vehicle battery lifetime

To maximise the resource efficiency of electric vehicle lithium-ion batteries (LIBs), their lifetimes can be extended through cascading second- and third-life applications. Using expert input, this study establishes a conceptual model for understanding these applications’ state of health (SOH) thresholds and user requirements. Using a qualitative methodology, including focus group discussions with multistakeholder experts and policy analysis of the European battery regulatory landscape, we propose extending LIB use. Our model outlines potential second- and third-life applications aiming to maximise battery value retention. The findings highlight gaps in current European Union regulations that inadequately support battery-repurposing strategies. The conceptual model with an SOH threshold and key performance indicators serves as a foundation for researchers and industries to explore cascading battery applications, foster long-term resource efficiency and contribute to the circular economy by extending LIB lifespans through repurposing initiatives.

A nonlinear model predictive control for air suspension of hub motor electric vehicle based on hybrid H2/H∞ state estimation

To alleviate the vibration from the road irregularities and the unbalanced electromagnetic forces (UEFs), deteriorating the dynamic performance of hub motor electric vehicles (HM-EVs), a novel nonlinear model predictive control (NMPC) is proposed based on a hybrid H2/H ∞ filtering algorithm for the HM-EVs with air suspension (HM-AS). The dynamic HM-AS model involves the electric vehicle’s longitudinal, lateral, and vertical motion. The air suspension system between the body and the hub motor enhances ride quality and insulates the oscillation delivered to the vehicle body. Then, the dynamic HM-AS model is validated by the actual vehicle test, and the proposed filter is developed to obtain the vehicle states. Besides, the NMPC controller guarantees the dynamic performance of HM-AS by intervening in vehicle attitude, road-holding stability, motor safety, and ride comfort. Finally, compared with the application of PID and MPC, the efficiency of the proposed method is demonstrated based on several random road scenarios. The results indicate the proposed algorithm can alleviate the dynamic performance of HM-AS and reduce motor vibration.

A fusion algorithm of multidimensional element space mapping architecture for SOC estimation of lithium-ion batteries under dynamic operating conditions

Accurate estimation of the state of charge (SOC) is pivotal for ensuring the reliable operation of lithium-ion batteries in electric vehicles, particularly under complex real-world conditions. This paper introduces a novel SOC estimation method named Multidimensional Elemental Space Mapping Architecture (MESMA), which integrates the battery equivalent circuit model with a data-driven approach to tackle adaptability under intricate conditions and high-dimensional input data correlation. Initially, a least squares method incorporating a forgetting factor is employed to identify the battery circuit model parameters and analyze their correlation with SOC. Subsequently, a feature fusion algorithm is introduced to discern spatial variables highly correlated with SOC by leveraging both original voltage and current data and circuit model parameters as inputs to a Convolutional Neural Network (CNN) model, and an eight-input and one-output spatially mapped feature CNN model is established. Finally, the XGBoost model is harnessed for battery SOC estimation. The effectiveness and adaptability of MESMA are validated using experimental data from both public single condition and mixed condition datasets, including FUDS, Mixed-1, etc. The results unequivocally demonstrate that MESMA accurately estimates the SOC of lithium-ion batteries under varying operating conditions. Furthermore, it exhibits remarkable adaptability and stability across different temperatures, effectively capturing SOC trends.

Comparison of Effects of Partial Discharge Echo in Various High-Voltage Insulation Systems

In this article, an extension of a conventional partial discharge (PD) approach called partial discharge echo (PDE), which is applied to different classes of electrical insulation systems of power devices, is presented. Currently, high-voltage (HV) electrical insulation is attributed not only to transmission and distribution grids but also to the industrial environment and emerging segments such as transportation electrification, i.e., electric vehicles, more-electric aircraft, and propulsion in maritime vehicles. This novel PDE methodology extends the conventional and established PD-based assessment, which is perceived to be one of the crucial indicators of HV electrical insulation integrity. PD echo may provide additional insight into the surface conditions and charge transport phenomena in a non-invasive way. It offers new diagnostic attributes that expand the evaluation of insulation conditions that are not possible by conventional PD measurements. The effects of partial discharge echo in various segments of insulation systems (such as cross-linked polyethylene [XLPE] power cable sections that contain defects and a twisted-pair helical coil that represents motor-winding insulation) are shown in this paper. The aim is to demonstrate the echo response on representative electrical insulating materials; for example, polyethylene, insulating paper, and Nomex. Comparisons of the PD echo decay times among various insulation systems are depicted, reflecting dielectric surface phenomena. The presented approach offers extended quantitative assessments of the conditions of HV electrical insulation, including its detection, measurement methodology, and interpretation.