Search Results (12,196)

Lithium-ion batteries are widely used in portable devices and electronic vehicles (EVs) due to their excellent performance. Because of their internal chemistry, these batteries have non-linear characteristics, their parameters being dependent on temperature and varying over time due to aging. Since electric vehicles are marketed in different regions of the globe with different climates, this has led to increased attention to the problem of the reduced performance of EVs in colder environments. The purpose of this research is to study the effects of preconditioning on Li-ion cells and determine the need for preconditioning in EVs that operate under low-temperature conditions. Additionally, based on the results, alternative coping strategies are also suggested which can be used instead of preconditioning when this is not a viable option. Given this, the 18650 Li-ion cells studied were divided into two categories, cells to be charged/discharged permanently at low temperatures and cells that were to be exposed to the same low temperatures but then preconditioned to ambient temperature before the charge/discharge cycle for a total of 100 performed cycles. It was observed that low temperatures have a direct negative impact on the usable capacity of the cells, accounting for a drop of 8% of the initial value. These effects can be completely negated by preconditioning the cells prior to charging/discharging. After that, the effects of medium-term storage on the capacity of the batteries were investigated to study the possible recovery in the capacity of the cells. Finally, the need for preconditioning the cells is analyzed and alternative methods to mitigate the issues are suggested for equipment where preconditioning is not possible. Full article

In recent years, the installed capacity of renewable energy sources, such as wind power and photovoltaic generation, has been steadily increasing in power systems. However, the inherent randomness and volatility of renewable energy generation pose greater challenges to grid frequency stability. To address this issue, this paper first introduces the Minkowski sum algorithm to map the feasible regions of dispersed individual units into a high-dimensional hypercube space, achieving efficient aggregation of large-scale schedulable capacity. Compared with conventional geometric or convex-hull aggregation methods, the proposed approach better captures spatio-temporal coupling characteristics and reduces computational complexity while preserving accuracy. Subsequently, aiming at the coordination challenge between day-ahead planning and real-time dispatch, a “hierarchical coordination and dynamic optimization” control framework is proposed. This three-layer architecture, comprising “day-ahead pre-dispatch, intraday rolling optimization, and terminal execution,” combined with PID feedback correction technology, stabilizes the output deviation within ±15%. This performance is significantly superior to the market assessment threshold. The research results provide theoretical support and practical reference for the engineering promotion of vehicle–grid interaction technology and the construction of new power systems. Full article

Considering the nonlinear trends, multi-scale variations, and capacity regeneration phenomena exhibited by battery capacity degradation under real-world conditions, accurately predicting its trajectory remains a critical challenge for ensuring the reliability and safety of electric vehicles. To address this, this study proposes a hybrid prediction framework based on Variational Mode Decomposition and a Transformer–Long Short-Term Memory architecture. Specifically, the proposed Variational Mode Decomposition–Transformer for Time Series–Long Short-Term Memory (VMD–TTS–LSTM) framework first decomposes the capacity sequence using Variational Mode Decomposition. The resulting modal components are then aggregated into high-frequency and low-frequency parts based on their frequency centroids, followed by targeted feature analysis for each part. Subsequently, a simplified Transformer encoder (Transformer for Time Series, TTS) is employed to model high-frequency fluctuations, while a Long Short-Term Memory (LSTM) network captures the long-term degradation trends. Evaluated on charging data from 20 commercial electric vehicles under a long-horizon setting of 20 input steps predicting 100 steps ahead, the proposed method achieves a mean absolute error of 0.9247 and a root mean square error of 1.0151, demonstrating improved accuracy and robustness. The results confirm that the proposed frequency-partitioned, heterogeneous modeling strategy provides a practical and effective solution for battery health prediction and energy management in real-world electric vehicle operation. Full article

The environment of today’s companies is marked by increasing dynamism. Rapid technological developments, strong innovation impulses, and continual market entry of new competitors create volatile conditions that make the delivery of valuable products challenging. Long-term corporate success therefore depends on offering a product portfolio consistently aligned with evolving market needs. Customers expect products that show continuous improvements in performance and functionality over time, making systematic product upgrading a key success factor. Release planning addresses this need by enabling continuous product evolution through planned product upgrades. It focuses on selecting and combining functional units for structured publication within releases. This proactive management of product value offers substantial potential but also demands comprehensive know-how, particularly given rising product complexity and the interplay of multiple technologies. The objective of this work is to develop a methodology that supports effective planning of product upgrades. The method assists in the product-specific selection of release types and the derivation of suitable release strategies. It yields release units defined by product structure and provides recommendations for appropriate release strategies. The methodology is demonstrated through its application to an electric vehicle, illustrating its practical relevance for software-intensive products. Full article

Deploying independent plug-in chargers, wireless chargers and auxiliary power modules within a single Electric Vehicle (EV) leads to an increased system complexity, higher component count and reduced power density. Integrated charger architectures address these limitations by unifying multiple charging and power conversion functions within a common hardware framework. Such integration reduces hardware redundancy, improves volumetric efficiency and enables more compact and cost-effective EV designs. Recent studies have explored a wide range of integrated charger topologies, targeting improvements in power density, cost and charging flexibility, often involving trade-offs such as reduced efficiency in exchange for smaller size or lower complexity. This paper presents a review of recent integrated charging topologies for EV applications, emphasizing system-level insights, design trade-offs, emerging trends and key technical challenges with the objective of guiding the development of efficient and scalable next-generation EV charging systems. Full article

This paper presents a method for modeling and predicting ISC in gel-electrolyte lithium-ion batteries, addressing critical safety concerns in electric vehicles. While gel-electrolytes are highlighted for their superior stability and performance advantages over liquid-electrolytes, they remain susceptible to IISC due to factors such as dendrite formation or mechanical stress. This study provides a detailed analysis of the unique ISCs mechanism in gel-electrolytes, emphasizing the differences between gel-electrolyte and liquid-electrolyte batteries in terms of ion transport dynamics and thermal performance. Based on these characteristics, an electrochemical–thermal–ISC coupling model was developed, and an external short-circuit resistance test was conducted to validate the model’s accuracy. By simulating various ISC states using the coupling model, a comprehensive dataset of battery ISC parameters was obtained, encompassing voltage, current, temperature, SOC, capacity loss, and internal resistance. ISC prediction models were subsequently developed using BP, CNN, and LSTM networks, with a comparative analysis of their prediction accuracy. This research advances the ISC prediction framework for gel-electrolyte batteries and demonstrates the potential of CNN-based models to achieve higher accuracy in fault prediction. Accurate ISC prediction is crucial for ensuring safe battery operation in electric vehicles. Full article

The rapid development of electric vehicles (EVs) will inevitably consume substantial scarce resources, posing risks and challenges to their supply chains. From a life cycle perspective, this study innovatively incorporates charging piles (CPs) into the research scope. Six scenarios are established to quantitatively analyze the scrap and recovery volume of 20 metallic and 3 non-metallic strategic mineral resources in lithium-ion batteries (LIBs) and CPs for China’s passenger EVs during 2010–2050. Under six scenarios, the results show that Al in LIBs and Fe in CPs have the highest scrap volumes, increasing from 2.69 t in 2010 to 2.98 × 10⁶ t in 2050 and from 34.76 t in 2024 to 1.14 × 10⁶ t in 2050, respectively. In contrast, Co in LIBs and Zr in CPs have the smallest scrap volumes, increasing from 0.22 t in 2012 to 8.25 × 10⁴ t in 2050 and from 8.8 × 10⁻⁷ t in 2024 to 1.52 × 10⁻⁵ t in 2050, respectively. Over 97% of Li, Co, Ni, and Al originates from LIBs during 2026–2050, while Fe and Cu from CPs show notable growth, underscoring recycling urgency. Recycle-demand analysis in LIB reveals the gap rate for nine elements. Seven elements’ gap rates are 0.39–0.81 (GI = 80%) and 0.25–0.75 (GI = 100%), while Fe’s gap rate turns to 0 in 2045 due to LFP phase-out and P’s gap rate reaches −1.22 (GI = 80%) and −1.77 (GI = 100%) in 2045 before rebounding. Full article

Amid the growing global environmental challenges, precise and efficient vehicle emission management plays a critical role in achieving energy-saving and emission reduction goals. At the same time, the rapid development of connected vehicles and autonomous driving technologies has generated a large amount of high-dimensional vehicle operation data. This not only provides a rich data foundation for refined emission accounting but also raises higher demands for the construction of accounting models. Therefore, this study aims to develop an accurate and efficient emission accounting model to contribute to the precise nitrogen oxide (NO_x) emission accounting for hybrid electric vehicles (HEVs). A systematic approach is proposed that combines incremental dimensionality reduction with advanced regression algorithms to achieve refined and efficient emission accounting based on multiple variables. Specifically, the dimensionality of the real driving emission (RDE) data is first reduced using the feature selection and t-distributed stochastic neighbor embedding (t-SNE) feature extraction method to capture key parameter information and reduce subsequent computational complexity. Next, an incremental dimensionality reduction method based on dictionary learning is employed to efficiently embed new data into a low-dimensional space through straightforward matrix operations. Given the computational cost of the dictionary learning training process, this study introduces the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) for accelerated iterative optimization and enhances the computational efficiency through parameter optimization, while maintaining the accuracy of dictionary learning. Subsequently, an NO_x emission factor correction factor prediction model is trained using the low-dimensional data obtained from t-SNE embeddings, enabling direct computation of the corresponding correction factor when presented with new incremental low-dimensional embeddings. Finally, validation on independent HEV datasets shows that parameter K improves to 1 ± 0.05 and R² increases up to 0.990, laying a foundation for constructing an emission accounting model with broad applicability based on multiple variables. Full article

Determinants of consumers’ decisions in the electric vehicle market are dictated by many factors, starting from ecology to the profitability of owning an electric vehicle. Currently, the electric vehicle market in Poland grows every year. When addressing the issues related to the determinants of consumer decisions on the electric vehicle market, statistical data and an online questionnaire were used, in which 103 people, who were interested in electric vehicles, participated. The main purpose of this research was to determine what factors influence consumers’ attitude to the purchase of electric vehicles the most. The study focuses primarily on Battery Electric Vehicles (BEVs), as reflected in the survey design and respondents’ interpretations of electric vehicles. The study showed that over half of the respondents are considering the purchase of an electric vehicle, and to purchase this type of car they would be more encouraged by financial support, such as subsidies from the state and tax relief, as well as free parking spaces in cities. It was also established that consumers are discouraged from buying electric vehicles by the lack of adequate infrastructure in cities needed to freely own an electric vehicle, as well as too high prices of these cars and the long time it takes to charge the battery. Full article

Thermal control represents one of the most important parameters influencing the safety and reliability of lithium-ion batteries, especially at high rates required for modern electric vehicles. The present paper investigates the thermal and electrothermal performance of a lithium iron phosphate (LiFePO₄) battery pack using a combination of experimental, statistical, and numerical methods. The 8S5P module was assembled and examined under load tests of 200, 400, and 600 W with and without active air-based cooling. The findings indicate that cooling reduced cell surface temperature by up to 10 °C and extended discharge time by 7–16% under various load conditions, emphasizing the effect of thermal management on battery performance and safety. In order to more systematically investigate the impact of ambient temperature and load, a RSM study with a central composite design (CCD; 13 runs) was performed, resulting in two very significant quadratic models (R² > 0.98) for peak temperature and discharge duration prediction. The optimum conditions are estimated at a 200 W load and an ambient temperature of 20 °C. Based on experimentally determined parameters, a finite-element simulation model was established, and its predictions agreed well with the measured results, which verified the analysis. Integrating measurements, statistical modeling, and simulation provides a tri-phase methodology to date for determining and optimizing battery performance under the electrothermal dynamics of varied environments. Full article

Most electric vehicles use 400 V batteries, while some companies are moving to 800 V to reduce current in electric drives. More cars are expected to adopt 800 V at the DC terminals of the batteries, but 400 V will remain common for the duration of this transition, so future off-board chargers must support a wide voltage output range. Silicon carbide switches are used to keep the power–electronics interface compact and scalable. The AC/DC stage of a modular silicon carbide-based interface is designed using a T-type active front end and a dual active bridge. The T-type front end is optimized with a genetic algorithm. The resulting model is used to tune the inner current and outer voltage controllers. Bode analysis shows an inner current loop bandwidth of 4.25 kHz with a phase margin of ${53}^{\circ }$ and a gain margin of 30 dB. The outer voltage loop reaches 50 Hz with a phase margin of ${108}^{\circ }$ and a gain margin of 33 dB. The controller is implemented on a dSPACE MicroLabBox. Tests show peak efficiency of 98.5% in G2V mode and 98.3% V2G mode. THD stays under 5% above 4 kW and reaches 3% at peak power. Full article

In response to the urgent need for decarbonising the transport sector, this paper analyses the thermal performance of a battery electric minibus under cold ambient conditions. Thermal simulation models of the vehicle cabin and its electric heating circuits for both driver and passenger areas were developed using Modelica and validated with measurement data at −7 °C and 0 °C. The model showed good agreement with the measurements, with cabin temperature deviations within ±1.6 K and heating power deviations below 6%. Results show that the existing electric-only heating system is, in the automatic heating mode selected, insufficient to reach the target cabin temperature of 23 °C, as the optional fuel-powered heater was omitted to ensure fully zero-emission operation. To address this, an extended heating system with an additional heat exchanger was implemented in the simulation, which improved the overall cabin temperature level and also its spatial variation. However, it also increased the heating power demand by 43% at −7 °C (from 4.8 kW to 6.8 kW) and by 17% at 0 °C (from 4.8 kW to 5.6 kW). An additional heat loss analysis revealed that approx. 65–75% of all thermal losses occur through the window areas. Future improvements should therefore focus on optimising the heating strategy and enhancing cabin and heating system insulation to reduce energy demand while maintaining or even improving passenger comfort. Full article

With the expanding electric vehicle market, there is increasing demand for improved battery safety and fast-charging performance. Ceramic-based solid electrolytes have attracted attention due to their high thermal and electrochemical stabilities. Li-glass solid electrolytes (e.g., Li₂O–LiCl–B₂O₃–Al₂O₃, LCBA) are promising materials because they enable low-temperature sintering (<550 °C), suppress lithium volatilization, mitigate ionic conductivity degradation, and enable cost-effective manufacturing. LCBA can be co-sintered with graphite anodes to form composite anode materials for LCBA-based all-solid-state batteries. However, insufficient densification and shrinkage mismatch often lead to limited ionic conductivity and interfacial delamination. In this study, the sintering behavior of LCBA was investigated using a hot-press-assisted process, and LCBA/graphite composite anodes were co-sintered to evaluate their electrochemical and interfacial properties. The LCBA electrolyte sintered at 550 °C exhibited high densification and an ionic conductivity of 3.86 × 10⁻⁵ S cm⁻¹. Additionally, the composite containing 50 wt% LCBA achieved a maximum tensile stress of ~0.23 MPa and a high interfacial fracture energy of ~180–200 J m⁻², indicating enhanced deformation tolerance and fracture resistance. This approach improves the densification, ionic conductivity, and interfacial mechanical stability of LCBA solid electrolytes and their composite anodes, highlighting their potential for next-generation all-solid-state secondary battery applications. Full article

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon. Full article

Research on road transport infrastructures is rapidly evolving as electrification, automation, and digital connectivity reshape how systems are designed, operated, and managed. This study presents a combined bibliometric, scientometric, and network analysis of 2755 publications published between 2021 and 2025 to map the intellectual structure, main contributors, and dominant technological themes shaping contemporary road transport research. Using data from the Web of Science Core Collection, co-occurrence mapping, thematic analysis, and collaboration networks were generated using Bibliometrix and VOSviewer. The results reveal strong growth in research output, with China, the United States, and Europe forming the core of high-impact publication and collaboration networks. Six bibliometric clusters were identified and consolidated into three overarching domains: road transport systems, emphasizing vehicle dynamics, control, and real-time computational frameworks; energy and efficiency-oriented mobility research, focusing on electrification, optimization, and infrastructure integration; and emerging digital technologies, including IoT, AI, and autonomous vehicles. The analysis highlights persistent research gaps related to interoperability, cybersecurity, large-scale deployment, and governance of intelligent transport infrastructures. Overall, the findings provide a data-driven overview of current research priorities and structural patterns shaping next-generation road transport systems. Full article