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In traditional industry setups, Automated Guided Vehicles (AGVs) follow trajectories planned together with the layout of the storage or production facility and supported by fixed markers on the floor or on the walls. Traffic rules manage the avoidance of multiple vehicles, while fleet management gets movement and transportation commands completed as soon as possible. In contrast, recent developments in navigation and advanced computing, sensor, and communication capabilities make their free movement safe and manageable. Detailed route planning and scheduling can guarantee that the vehicles keep a safe distance in time and space. A recent challenge of electric AGVs is that their charging may take several hours, which must be factored into their schedule. This has made minimal energy demand a key objective alongside earliest delivery and strictly meeting the deadlines. This paper presents a method for detailed routing and scheduling of AGV fleets to minimize energy consumption while considering battery levels and charging times. The optimization method is illustrated by a case study where multiple delivery tasks are performed by synchronized movement of vehicles on a complex warehouse layout. In the optimal solution, the scheduled waiting times for collision avoidance are utilized by the vehicles to pre-charge their batteries. Full article

Vehicle-to-grid (V2G) services are gaining attention as a strategy to integrate electric vehicles (EVs) into sustainable energy systems. Although technological aspects have been widely studied, methodologies for identifying optimal V2G hubs and forecasting the energy available for grid transfer remain limited. This study introduces a data-driven approach to (i) identify the optimal V2G region based on the aggregated parking duration using floating car data (FCD; collected from GPS-enabled vehicles); (ii) estimate the surplus battery capacity of electric vehicles in that region; and (iii) forecast the energy transferable to the grid. The methodology applies spatial k-means clustering to define candidate zones, computes aggregated parking durations, and selects the optimal hub. The surplus energy is estimated considering the daily mobility needs of users, 20% reserve, and transfer rates. For forecasting, autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are implemented and compared. The proposed methodology has been applied to a real case study, using 58 days of FCD observations. The empirical findings of this study show the goodness of the proposed methodology, and the opportunity offered V2G technology to support the sustainable use of energy. The ARIMA model demonstrated a superior forecasting performance with an RMSE of 52.424, MAE of 36.05, and MAPE of 12.98%, outperforming LSTM (RMSE of 99.09, MAE of 80.351, and MAPE of 53.20%) under the current data conditions. The results of this study suggest that for supporting grid needs of a medium-sized city, V2G plays a key role, and at the current status of the EV penetration, the use of FCD and predictive approaches is paramount for making an informed decision. Full article

With the increasing deployment of lithium iron phosphate (LiFePO₄) batteries in electric vehicles and energy storage systems, the recycling of these materials has become an urgent necessity. Specifically, the reclamation of lithium iron phosphate cathode materials presents a significant challenge in the recycling process. In this study, we proposed an efficient low-temperature hydrothermal direct regeneration method aimed at repairing lithium vacancies and Fe/Li inversion defects in spent lithium iron phosphate resulting from prolonged cycling. By using this method, spent lithium iron phosphate was successfully regenerated through a hydrothermal process conducted at 80 °C for 6 h, utilizing hydrazine hydrate (N₂H₄·H₂O) as a potent reducing agent and lithium hydroxide (LiOH·H₂O) as the lithium source. X-ray diffraction (XRD) analysis, coupled with Rietveld refinement, revealed a substantial reduction in the concentration of Fe/Li anti-site defects in the spent material, decreasing from 8.8% to 3.3% following regeneration. Consequently, the electrochemical performance was significantly restored. The initial specific discharge capacity increased from 118.0 mAh·g⁻¹ to 150.3 mAh·g⁻¹, and the capacity retention after 100 cycles (at 1 C) improved from 67.5% to 90.7%. The hydrothermal regeneration process introduced in this work effectively repairs the material structure and restores the active valence state of iron, thereby significantly enhancing lithium-ion diffusion and electron transport capabilities. This approach constitutes a technically viable solution for the efficient, environmentally friendly, and cost-effective recycling of spent lithium-ion batteries. Full article

Community energy systems are expected to play an increasingly important role in the decarbonization of the residential sector, but their operation depends on how different electricity and heat storage technologies are configured and used. Existing studies typically examine storage options in isolation, limiting the comparability of their operational roles. This study addresses this gap by developing a decision-support framework that enables a consistent, operation-focused comparison of battery energy storage, hydrogen storage, and electric-vehicle-based storage within a unified community-scale hybrid energy system. The model represents electricity and heat balances in a hub formulation that couples photovoltaic and wind generation, a gas engine, an electric boiler, thermal and electrical storage units, hydrogen conversion and storage, and an aggregated fleet of electric vehicles. It is applied to a stylized Polish residential community using local demand, generation potential, and electricity price data. A set of single-technology and multi-technology scenarios is analyzed to compare how storage portfolios affect self-sufficiency, self-consumption, grid exchanges, and operating costs under current electricity market conditions. The results show that battery and electric vehicle storage primarily provide short-term flexibility and enable price-driven arbitrage, as reflected in the highest contribution of battery discharge to the electricity supply structure (5.6%) and systematic charging of BES and EVs during low-price hours, while hydrogen storage supports intertemporal shifting by charging in multi-hour surplus periods, reaching a supply share of 1.4% at the expense of substantial conversion losses. Moreover, the findings highlight fundamental trade-offs between cost-optimal, price-responsive operation and autonomy-oriented indicators such as self-sufficiency and self-consumption, showing how these depend on the composition of storage portfolios. The proposed framework, therefore, provides decision support for both technology selection and the planning and regulatory assessment of community energy systems under contemporary electricity market conditions. Full article

Heavy-duty vehicles dominate global freight movement and primarily rely on fossil-derived diesel fuel. However, fluctuations in crude oil prices and evolving emissions regulations have prompted interest in alternative powertrains to enhance fleet energy resiliency. This study paired real-world operational data from a large commercial fleet with high-fidelity vehicle models to evaluate the potential for replacing diesel internal combustion engine (ICE) trucks with alternative powertrain architectures. The baseline vehicle for this analysis is a diesel-powered ICE truck. Alternatives include ICE trucks fueled by bio- and renewable diesel, compressed natural gas (CNG) or hydrogen (H₂), as well as plug-in hybrid (PHEV), fuel cell electric (FCEV), and battery electric vehicles (BEV). While most alternative powertrains resulted in some payload capacity loss, the overall fleetwide impact was negligible due to underutilized payload capacity for the specific fleet considered in this study. For sleeper cab trucks, CNG-powered trucks achieved the highest replacement potential, covering 85% of the fleet. In contrast, H₂ and BEV architectures could replace fewer than 10% and 1% of trucks, respectively. Day cab trucks, with shorter daily routes, showed higher replacement potential: 98% for CNG, 78% for H₂, and 34% for BEVs. However, achieving full fleet replacement would still require significant operational changes such as route reassignment and enroute refueling, along with considerable improvements to onboard energy storage capacity. Additionally, the higher total cost of ownership (TCO) for alternative powertrains remains a key challenge. This study also evaluated lifecycle impacts across various fuel sources, both fossil and bio-derived. Bio-derived synthetic diesel fuels emerged as a practical option for diesel displacement without disrupting operations. Conversely, H₂ and electrified powertrains provide limited lifecycle impacts under the current energy scenario. This analysis highlights the complexity of replacing diesel ICE trucks with admissible alternatives while balancing fleet resiliency, operational demands, and emissions goals. These results reflect a US-based fleet’s duty cycles, payloads, GVWR allowances, and an assumption of depot-only refueling/recharging. Applicability to other fleets and regions may differ based on differing routing practices or technical features such as battery swapping. Full article

Lithium-ion batteries are extensively employed in new energy vehicles, where accurate State of Charge (SOC) estimation is fundamental for optimal battery management. However, existing methods often rely on single-model approaches and fail to leverage the complementary advantages of multiple models. This study proposes an innovative hybrid estimation model integrating a Temporal Convolutional Network (TCN) that efficiently captures long-range temporal dependencies via dilated convolution and residual blocks, with a Bidirectional Long Short-Term Memory Network (BiLSTM) that extracts bidirectional context information to enhance the accuracy of SOC estimation. First, the Panasonic datasets are utilized, with current, voltage, and cell temperature selected as input features. Subsequently, the proposed model is evaluated under various temperature conditions and driving cycles, demonstrating high accuracy and robustness. Finally, comparative experiments are conducted against traditional methods, such as standalone TCN and Long Short-Term Memory (LSTM) networks, under both 10 °C and −10 °C operating conditions. The results show that the hybrid model achieves superior performance in error metrics. Specifically, based on a second-order resistor-capacitor network, at −10 °C, the Root Mean Squared Error is reduced by 0.948%, and at 10 °C, it decreases by 0.398%. Additionally, the Maximum Absolute Error is lowered by 2.751% at −10 °C and by 2.192% at 10 °C. These improvements highlight the model’s significant potential as an effective solution for SOC estimation in lithium-ion batteries. Full article

This paper presents two enhanced variants of the Artificial Protozoa Optimizer (APO), namely the Adaptive Balanced Artificial Protozoa Optimizer (AB-APO) and the Fractional Calculus-Enhanced Artificial Protozoa Optimizer (FC-APO), for optimal multi-Distributed Energy Resources (DERs) planning in smart radial distribution networks. The proposed framework addresses the coordinated allocation of Electric Vehicle Charging Stations (EVCSs), photovoltaic (PV) units, and Battery Energy Storage Systems (BESS). The AB-APO introduces an adaptive balancing mechanism that dynamically regulates exploration and exploitation to improve convergence stability and robustness, while the FC-APO incorporates fractional-order dynamics to embed long-memory effects, enhancing numerical stability and search smoothness. The proposed optimizers are evaluated on the IEEE-33 and IEEE-69 bus systems under eight DERs penetration scenarios. Simulation results demonstrate significant reductions in real and reactive power losses, improved voltage profiles, and effective mitigation of EV-induced network stress. Real power loss reductions exceeding 54%, 38.53%, 53.78%, 38.20%, 61.68%, and 60.72% are achieved for the IEEE-33 system, while reductions of 64.32%, 63.51%, 64.33%, 63.51%, 67.31%, and 67.04% are obtained for the IEEE-69 system across Scenarios 3–8. Overall, the results highlight the effectiveness of adaptive balancing and fractional-order modeling in strengthening APO-based optimization and confirm the suitability of the AB-APO and FC-APO as efficient planning tools for future smart distribution networks. Full article

For hybrid energy storage systems requiring efficient energy management to achieve optimal power allocation between the power battery and supercapacitor, this study proposes an optimal energy management method integrating whole-process particle swarm optimization with fuzzy logic control, which simultaneously considers braking safety and energy efficiency optimization. First, a zonal braking force distribution strategy based on the I-curve, ECE regulations curve, and front wheel lockup curve is designed to maximize energy recovery while ensuring braking safety. On this basis, a whole-process “driving–braking” fuzzy logic control strategy for power distribution is constructed, aiming at maximizing braking energy recovery efficiency and minimizing energy consumption per 100 km. The parameters of the membership functions in the fuzzy controller are optimized using the particle swarm optimization algorithm to achieve global optimization of the control process. Finally, simulation validation of the optimization results demonstrates that, compared with traditional logic threshold control under NEDC conditions, the proposed strategy improves braking energy recovery efficiency by 10.32%, reduces energy consumption per 100 km by 0.96 kWh, and decreases the peak current of the power battery by 6.4%, thereby effectively enhancing vehicle economy and extending battery lifespan. Full article

The vehicle-mounted flywheel battery is a complex assembly of multiple components that is subject to intense multi-physical field coupling and external disturbances, which lead to real-time changes in system parameters and reduce control performance. The aim of this study is to enhance the robustness and dynamic stability of the system under emergency avoidance conditions. Its internal multiphysics field coupling is intricate, and external disturbances further intensify the cross-coupling. Building upon this method, a highly robust control strategy with real-time coupling characteristic parameters is designed in this study. First, a bidirectional coupling method combining electromagnetism, heat, and structure fields was proposed. This method captured the dynamic interactions among the magnetic, thermal, and structural fields. Based on this analysis, a coupling characteristic function was extracted to quantify the real-time coupling strength. Then, this function was mapped into the parameters of the sliding mode controller. Adaptive gain adjustment can be achieved without relying on an accurate system model. The key assumptions include linear material properties within the operational temperature range and negligible unsteady turbulence effects in airflow. Full article

Accurate estimation of the State-of-Health (SOH) is essential for the reliable operation of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional data-driven models often lack interpretability and show limited robustness under non-linear aging conditions. In this study, a physics-informed Transformer model is proposed for long-term SOH estimation by incorporating physically interpretable, degradation-sensitive indicators into a self-attention framework. Incremental Capacity Analysis (ICA)-derived features and thermal-gradient indicators are used as auxiliary inputs to provide physics-consistent inductive bias, enabling the model to focus on degradation-relevant regions of the charging trajectory. The proposed approach is validated using four lithium-ion battery cells exhibiting diverse aging behaviors, including severe non-linear capacity fade. Experimental results demonstrate that the proposed model consistently outperforms an LSTM baseline, achieving an RMSE below 1.5% even for the most degraded cell. Furthermore, attention map analysis reveals that the model autonomously emphasizes voltage regions associated with electrochemical phase transitions, providing clear physical interpretability. These results indicate that the proposed physics-informed Transformer offers a robust and explainable solution for battery health monitoring under practical aging conditions. Full article

With the advent of electric vehicles, the demand for non-destructive inspection methods for battery evaluation has increased. Among various requirements, achieving high-frame-rate performance is particularly critical for rapid inspection in end-user systems. However, image delay, which increases with frame rate, has emerged as a significant challenge due to inherent limitations in sensor design. As a result, extensive research has been conducted to improve image lag performance. In this study, we conducted an in-depth analysis of the fundamental causes of image lag in image sensors. Based on these findings, we fabricated a novel sensor with a reset transistor separate from the readout transistor used for data transfer. This approach effectively increased the reset current of the photodiode, significantly reducing image lag. The transistor material used in this study was InGaZnO, which showed a significant improvement in image lag compared to conventional methods. By introducing a dedicated reset transistor, the allowable reset current of the PIN diode was increased by a factor of 100 compared to the ROIC-limited condition, resulting in a significant reduction in image lag from 3.8% (STS) to 0.9% (DTS) under high-frame-rate operation. This research provides a theoretical basis for proposing various new X-ray digital image sensor structures. Full article

Although the combination of solar power and electric vehicles is widely considered beneficial, practical applications reveal substantial variance. To determine the proportion of solar energy used for charging and to identify the main drivers of a high solar share, a dataset containing measured 5 min energy time series of 725 households with PV and EVs was analyzed. In the existing literature, this represents a novelty, as most studies in this field are simulation-based, rely on synthetic profiles, use lower time resolutions, or are based on questionnaires. The share of solar energy used for EV charging is highly dispersed and varies by about ±40% around a median of 60%. The analysis shows that clustering by preferred charging times has strong explanatory potential: at the median, EVs charged predominantly during the daytime achieve a solar share that is more than 40% higher than those charged in the evening. In the latter case, home battery storage increases the solar share by an average of 20 percentage points. A similar magnitude of a 25-percentage-point increase could be reached with solar surplus charging compared to uncontrolled charging. On average, households with PV, battery, and EVs cover more than 56% of their total demand with self-generated solar energy; with solar-adapted charging, median values exceed 77%. If a heat pump is used on site, the self-sufficiency decreases but can still reach median values above 45% and up to 61% for optimized households. Full article

The rapid expansion of new energy vehicles (NEVs) has intensified concerns over power battery safety, making consumer safety preferences an important driver of firms’ innovation and supply chain decisions. From the perspective of structural symmetry and structural asymmetry in supply chains, this study examines how consumer safety preferences shape innovation incentives and supply mode selection in the power battery supply chain. A game-theoretic framework is developed to analyze four representative supply modes characterized by different degrees of decision power and structural asymmetry, including in-house production, sourcing from a dominant supplier, sourcing from a non-dominant supplier, and equity-based cooperation. Stackelberg and Nash game models are employed to derive equilibrium pricing, innovation effort, recycling decisions, and profit allocation outcomes. Numerical simulations further explore the interaction effects between consumer safety preferences and key cost factors. The results show that stronger consumer safety preferences consistently promote battery innovation and enhance overall supply chain profitability, while the distribution of innovation gains depends critically on the underlying supply structure. Supply mode selection exhibits threshold effects as safety preferences increase, and innovation and recycling decisions respond asymmetrically. Moreover, innovation costs significantly moderate the impact of safety preferences on innovation effort, with the strength of this interaction varying across symmetric and asymmetric supply modes. These findings highlight the role of structural asymmetry in shaping innovation incentives and provide insights for firms and policymakers seeking to design effective supply chain governance mechanisms under rising safety concerns. Full article

Lithium-ion batteries are pivotal in powering modern technology, from electric vehicles to portable electronics. However, their safety is challenged by the risk of thermal runaway, a critical failure mode leading to catastrophic consequences such as fires and explosions. This study presents a machine learning framework for the early detection of thermal runaway events using sensor data from over 210 open-source battery tests. The framework utilizes voltage, temperature, and force measurements from experimental mechanical indentation tests, with force data providing additional predictive value beyond standard BMS sensors. Key features such as the rate of temperature change and voltage change were engineered from raw time-series data. An XGBoost classifier was trained to detect critical patterns up to 20 s in advance, with lead-time shifting applied to simulate real-time warnings. Critical conditions were operationally defined as temperature exceeding 80 °C or voltage dropping below 3.0 V. The model achieved an F1-score of 0.98 on a test set of 734k data points from 42 independent mechanical indentation battery tests (natural class distribution: 45% critical, 55% normal). SHAP analysis revealed that low voltage (below 3.0 V) and rapid temperature rise (above 80 °C/s) were the most influential features. The system identified patterns 5–10 s before threshold crossing, with a mean detection of 8.3 s. This research demonstrates the potential for machine learning-enhanced battery safety, providing a foundation for future advancements in the field. Full article

Unmanned Aerial Vehicles (UAVs) are emerging as a fundamental part of Flying Ad Hoc Networks (FANETs). However, owing to the limited energy capacity of UAV batteries, wireless power transfer (WPT) technologies have recently gained interest from researchers, offering recharging possibilities for FANETs. Based on this background, this study highlights the need for wireless charging to enhance the operational endurance of FANETs in Internet-of-Things (IoT) environments. This review investigates WPT power replenishment to explore the dynamic usage of UAVs in two ways. The former is for using a UAV as a mobile charger to recharge the ground nodes, whereas the latter is for WPT applications in in-flight (UAV-to-UAV) charging. For the two research domains, we describe the different methods of WPT and its latest advancements through the academic and industrial research literature. We categorized the results based on the power transfer range, efficiency, wireless charger topology (ground or in-flight), coordination among multiple UAVs, and trajectory optimization formulation. A crucial finding is that in-flight UAV charging can extend the endurance by three times compared to using standalone batteries. Furthermore, the integration of IoT for the deployment of a clan of UAVs as a FANET is rigorously emphasized. Our data findings also indicate the present and future forecasting graphs of UAVs and IoT-integrating UAVs in the global market. Existing systems have scalability issues beyond 20 UAVs; therefore, future research requires edge computing for WPT scheduling and blockchains for energy trading. Full article