Search Results (2,221)

Electric vehicle (EV) fires can rapidly affect smoke and thermal conditions in underground parking garages. In this study, fifteen PyroSim/FDS cases were screened, but quantitative analysis was restricted to four prescribed-extraction cases and one baseline-matched two-device spray pair. The 0.30 m production mesh was selected using characteristic-fire-diameter, geometric-resolution, and computational-cost criteria. A matched 0.20/0.30/0.50 m check yielded non-monotonic fixed-point responses; mesh independence was not established. Extraction cases were compared using 270–300 s means and the first downward crossing of a 10 m visibility reference. At the same nominal outflow of 10 m³/s, two 5 m/s surfaces produced lower M1 gas temperature and CO and higher visibility than one 10 m/s surface. Relocating the second spray device beneath the vehicle reduced the τ = 120–150 s mean M4 underside-region gas temperature from 776.5 to 103.4 °C, while M1 visibility remained about 0.22 m. Because the model lacks a physical make-up-air path and corresponding experiments were not reproduced, these findings are limited to local prescribed-boundary comparisons. Relevant experiments support the represented mechanisms but the results do not validate the absolute point values. The simulations do not demonstrate battery extinguishment, maintained tenability, or code compliance. Full article

The transport sector contributes nearly 20% of New Zealand’s total greenhouse gas emissions, making it crucial for interventions to meet the 2050 net-zero target. Transitioning to electric vehicles (EVs) presents a sustainable solution but poses challenges in electricity distribution due to unpredictable EV charging behaviours. This research addresses these challenges by developing three mathematical models that optimise EV charging patterns, manage power flow along distribution lines and incorporate battery storage systems. Using the Tāmaki area as a case study, the models analyse total energy demand and optimal battery storage size, revealing that a 3.49 MWh battery system could mitigate the projected 2040 peak daily grid energy demand of 541.5 MWh and avoid costly power line upgrades. The study also introduces a vehicle-to-grid (V2G) integration model, showcasing its potential to reduce grid dependence and improve energy utilisation. The findings provide critical insights for Auckland’s electricity distribution companies, supporting strategic asset upgrades and offering evidence-based guidance for government policies on EV adoption. In summary, this research provides innovative solutions for optimising EV charging infrastructure, benefiting utility companies and policymakers by informing data-driven decisions. The comprehensive approach, which includes power flow, battery storage, and V2G technology, presents a scalable framework for international cities facing similar challenges, promoting global sustainable transport solutions towards achieving international climate targets and sustainable urban development. Full article

Battery thermal management systems (BTMSs) are essential for maintaining the performance, efficiency, durability, and safety of electric-vehicle battery packs. Although fin-enhanced air-cooled BTMSs offer a simple and leakage-free cooling solution, their practical implementation is often limited by increased weight, insufficient temperature uniformity, and restricted heat-dissipation capability under high thermal loads. This study numerically investigates a compact air-cooled BTMS for two types of cylindrical lithium-ion batteries using aluminum and polypropylene (PP-$\beta$) circumferential fins in inline and staggered cell arrangements. Unlike previous fin-based air-cooling investigations, the present study combines a compact 2 × 4 battery pack with transverse and longitudinal center-to-center cell pitches of 1.2D, a direct comparison between metallic and lightweight polymer fins, and an assessment of two 18650 battery types with different capacities, thermophysical properties, and heat-generation characteristics. A three-dimensional steady-state conjugate heat-transfer model was developed in ANSYS Fluent to evaluate the effects of fin number, fin material, cell arrangement, ambient temperature, and inlet airflow velocity under discharge rates ranging from 1 C to 4 C. The results reveal that increasing the number of fins consistently reduced the maximum cell temperature but increased the pressure drop. The inline configuration generally achieved a lower maximum temperature and higher Nusselt number (Nu), whereas the staggered arrangement maintained a substantially lower pressure drop. Relative to the corresponding finless configurations, the Nu increased by 64.4–71.2% for the inline arrangement and 86.4–98.1% for the staggered arrangement. Polypropylene fins provided thermal performance close to that of aluminum fins in terms of maximum temperature while reducing the total fin mass by approximately 44.8%; however, aluminum fins maintained better temperature uniformity. These findings quantify the trade-offs among thermal performance, pressure drop, compact cell spacing, and system weight, providing design guidance for compact fin-enhanced air-cooled BTMSs. Full article

The practical deployment of aqueous zinc-ion batteries (AZIBs) is critically limited by uneven Zn²⁺ flux, uncontrolled dendrite growth, and concurrent parasitic reactions—notably the hydrogen evolution reaction (HER) and anode corrosion—arising from interfacial and kinetic instability during repeated plating/stripping cycles. These issues originate at the zinc anode–electrolyte interface, underscoring the necessity of advanced interfacial engineering. Here, we report a surface-confined polyphenol-derived interphase formed on zinc foil through a 1 min dip treatment in a dilute aqueous solution of a commercial tea polyphenol (TP) mixture (0.02 M); after rinsing and drying, the modified electrode is cycled in a conventional electrolyte to which no TP is deliberately added. This interphase promotes more homogeneous nucleation behaviour through coordination between phenolic oxygen-containing moieties and Zn²⁺, improves electrolyte contact homogeneity and perturbs the local water structure to mitigate water-mediated parasitic reactions. The TP-derived surface modification creates a substantially altered interfacial charging environment (Cdl = 47.25 vs. 16.83 µF cm⁻² for bare Zn) that facilitates more uniform zinc deposition. Symmetric cells with TP@Zn anodes demonstrated exceptional cycling stability exceeding 4000 h at 1 mA cm⁻² and 1 mAh cm⁻² (bare Zn fails within ~240 h under identical conditions), while TP@Zn//V₂O₅ full cells retained 56.2% capacity after 300 cycles at 0.5 A g⁻¹ with a higher median discharge voltage than bare Zn cells, substantially outperforming the latter (31.1% retention). Density functional theory calculations using the selected cluster models yield a markedly more negative electronic interaction energy for Zn²⁺ with an EGCG model ligand (−10.97 eV) than with H₂O (−4.49 eV), qualitatively supporting preferential coordination of Zn²⁺ by phenolic oxygen sites. This work presents a green, facile and potentially scalable interfacial regulation strategy and advances the understanding of natural polyphenols as pre-formed surface conditioners for highly reversible metal anodes. Full article

In this paper, a route- and environment-aware rule-based energy management system (EMS) strategy for EVs equipped with HESSs consisting of a lithium-ion battery and a supercapacitor is proposed. The proposed rule-based EMS integrates driving mode classification, traffic conditions, road gradient, wind resistance, ambient temperature, and the supercapacitor (SOC) to determine the optimal power-sharing strategy between the battery and the supercapacitor under various driving conditions, including city, highway, and stop-and-go traffic. A mathematical model of the EV powertrain, battery, and supercapacitor is developed. The performance of the proposed rule-based EMS strategy is validated in MATLAB/SIMULINK under three representative driving cycles: the Urban Dynamometer Driving Schedule (UDDS), Artemis Motorway 130, and a Central Business District (CBD) driving cycle. It is shown that the proposed rule-based EMS strategy significantly reduces the battery current stress while increasing supercapacitor usage. Full article

The increasing adoption of electric vehicles (EVs) has led to the generation of large-scale multi-domain datasets containing information on battery, thermal, charging, and driving behavior under real-world operating conditions. These datasets provide valuable insights into vehicle operation by enabling the analysis of interactions among electrical, environmental, and operational variables. This paper presents a comprehensive analysis of a real-world EV dataset collected during on-road operation, focusing on the characterization of the available measurements and their suitability for describing different vehicle operating conditions. The dataset is analyzed through statistical evaluation, correlation analysis, and dimensionality reduction techniques to investigate variable relationships and the representation of vehicle behavior. Specific operating conditions, including driving, charging, regenerative braking, and parking states, are described based on the available sensor measurements and reconstructed information where required. The results highlight the potential and limitations of real-world EV datasets, demonstrating the importance of data quality, variable consistency, and operational state reconstruction for accurately interpreting vehicle behavior and supporting future data-driven mobility applications. Full article

The design of biomass-derived polymer electrolytes with integrated multifunctionality represents a key strategy for sustainable energy storage devices. Here, we report a fully biomass-derived dual-network hydrogel electrolyte fabricated by combining Pectin (PC) and Chitosan (CTS), two naturally abundant polysaccharides. The Pectin/Chitosan dual-network hydrogel electrolyte (PC/CTS) forms a robust physically crosslinked network through electrostatic interactions between the carboxyl groups of PC and the amino groups of CTS, reinforced by dense hydrogen bonding and amide crosslinks, yielding a tensile strength of 77.76 MPa. The abundant polar functional groups of the dual polymer network serve a synergistic dual function: the amino groups of CTS preferentially adsorb onto the zinc anode surface (adsorption energy: −1.24 eV), forming a dynamic protective interphase, while the carboxyl groups of PC coordinate with Zn²⁺ (binding energy: −0.86 eV), reconstituting the solvation sheath and guiding uniform ion flux. This anchorage-capture mechanism, enabled by the molecular design of the polymer network, effectively suppresses dendrite growth, hydrogen evolution, and parasitic side reactions. Consequently, the PC/CTS electrolyte enables stable Zn//Zn cycling for 3350 h, 99.5% average Coulombic efficiency (CE) over 780 Zn//Cu cycles (at 5 mA cm⁻² and 1 mAh cm⁻²), and 63.8% capacity retention after 500 cycles in Zn//MnO₂ full cells. This work demonstrates that rational engineering of natural polymer networks can simultaneously address electrode stability challenges in aqueous batteries, offering a sustainable materials platform for next-generation energy storage devices. Full article

Decarbonizing heavy-duty transport in the mining sector requires a deep understanding of the interplay between specific energy consumption, payload dynamics, and ambient thermal stressors. This study presents an integrated, physics-informed machine learning framework to compare the energy intensity of battery-electric (EV) and diesel internal combustion engine (ICE) tippers on a real quarry route in Poland. We develop a bidirectional, route-aware model using physical force balance and high-resolution elevation data to estimate net energy consumption and regenerative braking potential over a complete closed-loop cycle. Furthermore, an Artificial Intelligence analysis utilizing a Random Forest regressor is implemented to simulate and quantify the non-linear impacts of ambient temperature, haul road rolling resistance, and payload mass on the specific energy intensity ($E_{spec}$). Results indicate that while EV energy demand surges in sub-zero climates due to parasitic battery thermal management loads, electric powertrains exhibit a profound thermodynamic advantage during loaded downhill segments, acting as net energy generators via recuperation. The proposed multi-factor approach provides a robust predictive tool for optimizing fleet deployment, infrastructure positioning, and decarbonization pathways in transitionary mining environments. Full article

This paper proposes a testable three-layer retained-state framework for intelligent energy systems grounded in mem-element theory. The framework distinguishes constitutive physical memory (Layer I), distributed circuit/converter memory (Layer II), and functional operational memory (Layer III) while preventing the indiscriminate classification of any history-dependent model as a mem-system. A retained variable is admissible only when it satisfies persistence, trajectory dependence, observable engineering consequence, and positive relevance beyond an instantaneous reference. Quantitative trajectory-separation, retained-state relevance, and engineering-gain indices, together with observability and falsification conditions, convert the framework from a taxonomy into a testable methodology. Layer I was partially validated using 636 experimental discharge cycles from four cells in the public NASA Ames PCoE Li-ion Battery Aging Dataset. Across 120 cycle–disjoint within-cell pairs matched at closely similar voltages, currents, temperatures, and local slopes, the median future-trajectory separation ($M_{\mathrm{T}\mathrm{S}}$) was 0.0688, the noise-normalized separation ($M_{\mathrm{N}\mathrm{S}}$) was 20.13, and the remaining-discharge duration differed by 150.7 s. Leave-one-battery-out prediction yielded positive retained-state relevance ($M_{\mathrm{R}\mathrm{I}}$ = 0.159 with a random-forest model; 95% bootstrap interval: 0.121–0.194). Two reduced-order cross-scale applications were then used for Layers II and III. In resonant wireless EV charging, retained-state augmentation reduced the efficiency RMSE by 29.9–32.2% and the current MAE by 27.5–27.9% in disturbed scenarios. In EV charging/V2G scheduling, history-aware operation reduced the charging cost by 3.3%, the degradation proxy by 12.0%, thermal-limit violations by 27.3%, and aggressive cycling by 17.2% while accepting lower peak reduction and V2G revenue. Same-information controls produced identical numerical outputs to the structured models by construction, showing that the framework’s novelty lies in admissibility, falsifiability, and cross-scale interpretation rather than privileged input information. The NASA study provides bounded public-experimental-data validation of Layer I; Layers II and III remain proof-of-concept demonstrations. Full article

Uncoordinated charging and discharging of large-scale electric vehicles (EVs) exacerbates grid peak–valley fluctuations, while deep discharging accelerates battery degradation. To address these challenges, this study proposes a coordinated charging–discharging optimization strategy integrating dynamic discharge restraint and a three-dimensional weighted comprehensive objective covering electricity price signals, grid operational constraints and battery health state. First, an EV travel behavior model is established to characterize spatiotemporal availability. Subsequently, a coupled battery aging model is developed by combining a power-law-based cycle aging formulation with a square-root calendar aging model, based on which an adaptive linkage mechanism between the depth-of-discharge upper bound and a net-revenue threshold is introduced. Building on these components, this model is constructed to jointly optimize three sub-objectives: charging station revenue maximization, battery lifetime cost minimization, and load fluctuation suppression, thereby mitigating grid peak–valley differences while reducing battery degradation and discharge costs. Multi-scenario simulations demonstrate that the proposed strategy, by coupling discharge restraint with spatiotemporal dynamic pricing, enables precise peak shaving of discharge power. For a fleet of 50 EVs, the charging station revenue reaches 1073.7 CNY, the grid peak–valley difference is reduced by 9.8%, and the battery degradation cost decreases by 23.1% compared with conventional strategies, corresponding to a carbon emission reduction of 386.4 tCO₂. When scaled to 100 EVs, the revenue increases by 101.9%, while the peak–valley difference is further reduced by 0.6%, demonstrating the effectiveness of the proposed strategy in enhancing economic performance, extending battery lifetime, and supporting grid stability. Full article

In this paper, we present LightBAL, an ultra-lightweight deep learning framework for real-time active cell balancing and onboard balancing control in electric vehicle (EV) battery management systems (BMSs). Although active cell balancing can improve battery utilisation and performance, applying deep learning-based balancing control strategies remains prohibitive in typical embeddable BMS platforms because of the computational complexity and inference latency of deep models. In response to this issue, we propose an AI-physics-informed controller that forecasts the voltage difference of a single cell, the SoC variation, and the optimal balancing current based on proportional feedback closed-loop (FCLL) control. The introduced framework exploits wavelet-based adaptive denoising, multi-scale hierarchical feature learning using a cooperative Principal Component Analysis (PCA) and autoencoder feature extraction technique, and a lightweight One-Dimensional Convolutional Neural Network (Conv1D) coupled with Bidirectional Long Short-Term Memory (BiLSTM) (Conv1D-BiLSTM). The implemented lightweight network is further trained by model compression methodologies such as knowledge distillation and 8-bit quantisation-aware training, aiming for efficient deployment on edge devices. Experimental validation on the multivariate battery time-series dataset demonstrates that LightBAL achieves an F1-score of 96.64%, a balancing efficiency of 94.30%, and a Mean Absolute Error (MAE) of 0.0379, outperforming methods based on conventional ANN, LSTM, and CNN. LightBAL without compression takes only 1.26 s to conclude on a PC workstation; the inference latency of the embedded light model is as low as 28.7 ms. In addition, hardware-in-the-loop (HIL) validation on the Raspberry Pi 4 platform indicates that the framework can fulfil real-time inference requirements under normal operating conditions, taking 28.7 ms per balancing process. Simulation shows that the proposed approach significantly decreases cumulative balancing energy loss by 12.4% across several driving cycle conditions. Full article

Accurate electrochemical impedance spectroscopy (EIS) characterization of low-impedance (<1 mΩ) lithium-ion battery cells used in electric vehicles is challenging because resistance and inductance in the measurement pathway can be comparable to the intrinsic cell impedance. This study investigates an EV-grade, large-format commercial lithium-ion pouch cell using a four-terminal EIS configuration and a custom connection fixture designed to maintain low and consistent contact resistance and reduce measurement-pathway effects. The EIS measurements shows good repeatability, and their linearity was confirmed using the Kramers–Kronig validity test. Impedance spectra acquired under selected state-of-charge (SOC) and temperature conditions were interpreted using an equivalent circuit model comprising an effective series inductance, an ohmic resistance, a constant phase element, a charge-transfer resistance, and a generalized Warburg element. Bayesian optimization followed by Nelder–Mead refinement was applied for parameter identification. The selected model represents the measured inductive, interfacial, and diffusion-related features as effective lumped responses rather than as a unique mechanistic decomposition. Across the SOC conditions investigated, the fitted ohmic resistance and charge-transfer resistance generally decreased as the temperature increased from 25 °C to 40 °C. By reducing the measurement errors related to the connection method rather than relying solely on instrument-side accuracy improvements, this work provides a practical approach for EIS measurements of milliohm-scale battery cells. These results establish a foundation for future temperature-compensated impedance diagnostics and further investigation of interfacial and transport behavior in EV battery cells. Full article

Amid global energy demand escalation, renewable energy intermittency, and electric vehicle (EV) charging demand concentration exacerbating power grid supply–demand contradictions, Vehicle-to-Grid (V2G) emerges as a solution, yet EV users’ V2G acceptance and participation willingness lack in-depth exploration. This study aims to fill this research gap by investigating Chongqing EV users’ V2G acceptance, behavioral intention, and influencing mechanisms to provide support for V2G promotion. It targets EV owners in Chongqing’s downtown areas, collecting 295 valid questionnaires, covering users’ demographics, travel-charging habits, and subjective attitudes. Based on technology acceptance and usage theories, it constructs a structural equation model (SEM) with perceived usefulness, ease of use, economic viability, and technological risk as latent variables to analyze their impacts on behavioral intention. Results show that perceived usefulness, perceived ease of use, and economic benefits positively affect behavioral intention, while technology risk perception exerts a negative effect; users with fixed commutes, low-range anxiety, and home charging piles are more receptive, and 70% support V2G but worry about battery wear and plug-in duration. Its innovation lies in integrating EV charging–discharging and travel patterns into the analysis, and its findings enrich new energy technology acceptance theory and provide a theoretical basis for transportation-energy system coordinated planning and V2G development. Full article

With the advancement of net-zero targets and the large-scale deployment of distributed photovoltaic (PV) systems, battery energy storage systems (BESS), and electric vehicles (EVs) in residential sectors, home energy management systems (HEMS) have become central to improving end-use energy efficiency. However, reliance on synthetic data, simplified and homogeneous load modeling, and lack of coordination between dynamic pricing mechanisms and multi-device scheduling, make them practically inefficient. Furthermore, most studies address only unilateral user-side optimization while neglecting the distribution network operational constraints and omitting rigorous anti-arbitrage mechanisms to preclude speculative user behavior. To address these gaps, this paper proposes a data-driven coordinated scheduling framework for residential PV-BESS and multi-device systems formulated within bi-level game-theoretic architecture. The upper-level employs particle swarm optimization (PSO) to determine dynamic additional price signals for peak shaving and distribution network security, with explicit constraints on distribution transformer capacity, node voltage deviation, and load ramp rate adapted to three-user scenarios. The lower-level formulates a mixed-integer quadratic programming (MIQP) model to achieve multi-objective optimization of user electricity cost, thermal comfort, device usage preference, battery cycle degradation, and end-of-cycle energy balance. Simulation results for representative summer and winter days indicate that the proposed framework reduces user-side electricity cost by around 30%, elevates PV self-consumption rate to over 70%, and achieves about 20% peak load reduction with around 15% peak-valley difference narrowing on the grid side. All distribution network security constraints and anti-arbitrage rules are strictly satisfied. The framework effectively reconciles the objectives of both residential users and the distribution grid. Full article

The electrification of road transportation has been widely proposed as a viable strategy for minimizing fossil fuel dependency and the environmental impacts of traditionally powered motor vehicles. This strategy has led to the increased adoption of electric vehicles (EVs), such as electric cars, electric buses (e-buses), electric trucks (e-trucks), etc., in cities, as they are more sustainable. Initiatives directed towards the electrification of vehicles can contribute towards sustainable transportation. One of these initiatives is the development of electric road systems (ERSs), which enable roadways to supply electric power to electric vehicles when the vehicles are in motion. The deployment of ERSs has developed as an alternative to address issues that negatively impact the adoption of EVs, such as long charging times, higher costs, short driving ranges, etc. Accordingly, this article explores the literature to understand how to ensure the reliable and safe operation of future electric road technologies and system deployments in municipalities. This study examines how ERSs power e-buses without relying solely on batteries. Additionally, this article analyzes the technological, legal, political, economic, and social factors that impact the electrification of road transport. Grounded in the literature, this study suggests that ERSs offer a cost-effective option to electrify heavy-duty transport, such as e-buses and e-trucks. The study provides insights for road authorities, municipalities, and policymakers to improve ERS deployment strategies, ensuring sustainable transportation while decarbonizing road transport. Full article