Search Results (2,596)

The precise detection and pose estimation of electric vehicle (EV) charging ports in unstructured outdoor environments remain challenging due to small sizes, variable illumination, and stringent tolerance requirements for robotic plug-in operations. To address these issues, this paper presents a hybrid perception framework that integrates an attention-embedded detection network with geometrically constrained pose optimization. For robust detection, CBAM-YOLOv11 is proposed, which incorporates a sequential channel-spatial attention module into the backbone network to enhance feature representation of texture-less small targets while suppressing background clutter and glare. Then, a topological geometric constraint-based method is developed for accurate pose estimation. Specifically, the 2D-3D correspondences are purified before being fed into an Efficient Perspective-n-Point (EPnP) solver, while a nonlinear refinement with rigid distance priors is applied as regularization. Extensive experiments on the dataset and a physical robotic platform demonstrate that the proposed detector achieves 99.2% mAP@0.5 and a 24.6 percentage point improvement in mAP@0.5:0.95 over the baseline YOLOv11. The pose estimation module reduces positioning standard deviations along the X, Y, and Z axes to 4.72 mm, 5.65 mm, and 5.60 mm, respectively, surpassing conventional EPnP by about 60%. In 30 repeated robotic insertion trials, the system attains a 93.3% success rate with approximately 78 ms, fully satisfying real-time and precision requirements for autonomous EV charging. Full article

The central challenge related to the development of electric vehicles (EVs) involves the effective integration of multiple input sources to create a robust and efficient power system. Recent advancements have focused on optimizing the power distribution within hybrid systems that combine batteries, supercapacitors, and renewable sources like solar PV. Conventionally, energy management systems (EMSs) have relied on rule-based algorithms or deterministic optimization methods. However, these techniques often lack adaptability under real-world driving conditions and face significant challenges regarding their generalizability and computational complexity when applied to dynamic driving cycles. Machine learning approaches are capable of modeling the complex, non-linear interactions between multiple energy sources to ensure intelligent power coordination. This paper proposes a novel Extreme Gradient Boosting (XGBoost)-based intelligent EMS for a BLDC motor-driven electric vehicle (e-bike) utilizing a hybrid battery–solar configuration with a supercapacitor for regenerative braking. The proposed system integrates a super-boost converter for efficient multi-source power delivery. The results show accurate energy source identification, effective multi-source coordination, improved energy utilization, reduced battery stress, and a rapid decision-making capability, which prove the feasibility of the proposed method for real-time electric bicycle energy management. The simulation was executed using the MATLAB/Simulink platform, and the obtained results are outlined. Full article

Improving the performance and lifespan of lithium-ion batteries is a key challenge for the development of electric vehicles. However, accurately estimating the state of charge (SoC), state of health (SoH), and life cycle remains complex due to the electrical, thermal, and aging phenomena associated with these energy storage systems. Against this backdrop, this study proposes a hybrid approach combining an electro-thermal model with a feedforward neural network (FNN) to improve the estimation of key lithium-ion battery performance indicators within a temperature range of 0 °C to 40 °C. The developed methodology was implemented in MATLAB/Simulink and applied to the analysis of the vehicle’s power profile, as well as the evolution of SoC, SoH, and battery life cycle. The results demonstrate an accuracy of 95.3% for state of charge (SoC) estimation, with a mean absolute error of 4.7%. For state of health (SoH) estimation, the accuracy is 95.8% accompanied by a mean absolute error of 4.2%. Lastly, for life cycle prediction, the accuracy is 92.5% with a mean absolute error of 7.5%. The performance results demonstrate the robustness of the proposed approach and its ability to replicate battery dynamic behavior under climatic conditions representative of the African context. This contribution opens up promising avenues for optimizing battery management systems and advancing the sustainable development of electric mobility. Full article

Accurate online state of health (SoH) estimation, especially for highly degraded second-life batteries (SLBs), is critical for the safe and efficient operation of any Battery Management System (BMS). However, existing data-driven estimation methods typically rely on complete charge/discharge cycles or assume a fixed starting state of charge (SoC) and fixed voltage windows. These assumptions are highly restrictive and rarely align with the random, partial charging behaviors of real-world electric vehicle (EV) operations and Battery Energy Storage System (BESS) applications. To overcome this limitation, this paper presents the following framework: an online SoH estimation approach utilizing long short-term-memory (LSTM)-ensembled learning using consecutive constant current (CC) charging segments. Instead of relying on a rigid voltage window, the presented method utilizes nested, expanding slices of highly flexible CC charging intervals to capture sequential degradation dynamics. Furthermore, this study maps suggestive ranges of optimal voltage intervals that dynamically adapt to different battery health conditions, ensuring high estimation accuracy despite the type of degradation. The framework is validated using four commercially available lithium-ion batteries (LIBs), with a moderate to low SoH down to ~40%. To ensure practical viability, the hybrid deep neural network (DNN) and LSTM architecture has been highly optimized, requiring only 474 trainable parameters. The framework has been evaluated utilizing multiple performance matrices, showing minimal discrepancy with excellent correlation to the true SoH throughout the lifespan of the testing cell. Full article

Dynamic inductive charging (DIC) combined with hybrid energy storage systems (HESSs) and vehicle-to-grid (V2G) capabilities offers a promising pathway toward extended-range electric vehicles with grid integration benefits. However, real-time optimal energy management remains challenging due to multi-axis coil misalignment, component aging, and bidirectional power flow uncertainty. This paper proposes an adaptive digital twin driven artificial intelligence (AI) energy management framework integrating physics-informed neural networks (PINNs), soft actor critic (SAC) deep reinforcement learning, and model predictive control (MPC) for optimal power distribution among a proton exchange membrane fuel cell (PEMFC), lithium-ion battery, supercapacitor, dynamic wireless charging, and grid interface in four-wheel drive electric vehicles (4WD-EVs). The framework features: (1) a self-evolving digital twin with online learning via Elastic Weight Consolidation (EWC) updating every 50 cycles; (2) a PINN-based state estimator for battery-state estimation, with an average inference time of 1.1 ms and a worst-case latency of 2.8 ms; (3) a hierarchical SAC–MPC strategy with high-level mode selection and low-level power optimization; (4) real-time five-degree-of-freedom WPT misalignment compensation, achieving a mean efficiency of 91.5% under the evaluated dynamic lateral misalignment conditions, with a 50 mm displacement amplitude; (5) degradation-aware V2G optimization generating €582.50/year in revenue while reducing battery aging by 31.8%; and (6) comprehensive techno-economic analysis yielding a discounted payback period of approximately 5.57 years and a net present value of approximately €3777 over a 10-year horizon. Validated through 200+ hours of hardware-in-the-loop (HIL) simulation on the dSPACE/NVIDIA Jetson platform, the proposed approach achieves a 24.3% cost reduction and 31.8% lower battery degradation. The MPC controller exhibits an average execution time of 32.1 ms, a 95th-percentile latency of 44.8 ms, and a worst-case latency of 62.4 ms, while remaining within the 100-ms real-time control deadline. Results demonstrate the viability of adaptive digital twins for next-generation EVs with autonomous charging and multi-source architectures. Full article

As the global stockpile of end-of-life electric vehicle batteries grows, subsidies have become a primary policy tool to stimulate recycling. However, recyclers can exploit private information about a battery’s State of Health to inflate quality claims and obtain excessive subsidies. This paper develops a Stackelberg game model encompassing a manufacturer and a recycler under three policy scenarios—no subsidy, subsidizing the manufacturer, and subsidizing the recycler—and extends the analysis to a hybrid subsidy scheme that optimally allocates a fixed subsidy budget between the two parties. The results deliver three explicit findings. First, subsidies exhibit a dual effect: while incentivizing recycling, excessively high subsidy levels can instead induce recycler misreporting and fail to improve environmental benefits, particularly for batteries with high reusable value. Second, the expected net value of recycled batteries is a pivotal parameter. When it is high, Pareto improvements for both supply chain members are achieved even with minimal subsidies, regardless of the subsidy recipient. Third, the proposed hybrid subsidy scheme, with an optimally derived allocation ratio, aligns the incentives of the manufacturer and the recycler, mitigates misreporting risk, and coordinates the supply chain more effectively than static, one-size-fits-all policies. The contribution of this study is twofold: it characterizes how subsidy allocation interacts with recycler misreporting in a battery recycling supply chain, and it provides policymakers with actionable guidance for designing differentiated and robust subsidy contracts that advance a sustainable battery ecosystem. Full article

Against the backdrop of the “dual-carbon” strategy (carbon peaking and carbon neutrality), countries worldwide are committed to advancing the application of new energy in the transportation sector. This has spurred the rapid development of electric vehicles (EVs) and led to higher requirements for the research and construction of charging infrastructure. To address the challenges of high daily power purchase costs and severe grid-connected power fluctuations in the daily scheduling of PV–storage–charging integrated stations, as well as the limitations of conventional particle swarm optimization (PSO) with random or chaotic initialization—including insufficient engineering prior knowledge of station time-of-use (TOU) electricity prices and energy storage state of charge (SOC), numerous inferior solutions in the initial population, and high susceptibility to premature convergence—this paper develops a dual-objective optimal scheduling model that balances daily power purchase cost and grid-connected power fluctuation. The model integrates PV output, EV charging loads, energy storage charge–discharge schedules, and multiple categories of operational constraints. Grounded in the economic operation principle of “valley-period charging and peak-period discharging”, an improved PSO algorithm with electricity PriceSOC joint guided initialization (PriceSOC-PSO) is proposed. High-quality initial particles are generated by setting segmented SOC targets, introducing random perturbations, and implementing closed-loop correction of the energy storage schedule, while hybrid random particles are incorporated into the population to preserve diversity. Multiple simulation scenarios, including the no-energy-storage case, standard PSO, chaotic-initialized PSO, the proposed PriceSOC-PSO, Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and the Sparrow Search Algorithm (SSA), are established to carry out objective weight sensitivity analysis and cross-algorithm comparative analysis. The results demonstrate that, compared with the no-energy-storage scenario, the proposed strategy reduces the daily power purchase cost and grid-connected power fluctuation by 11.7% and 74.9% respectively under the weight configuration $({\omega }_1=0.3, {\omega }_2=0.7)$. When the weight configuration is adjusted to (${\omega }_1=0.7, {\omega }_2=0.3)$, the two indicators are decreased by 14.8% and 62.6% respectively. Full article

The present study experimentally investigates the influence of n-octanol addition to Jet-A fuel on the combustion performance and emission behavior of a micro-turboprop-based hybrid UAV (“Unmanned Aerial Vehicle”) power system. The experiments were conducted on a dedicated hybrid propulsion test bench equipped with a KingTech micro-turboprop engine mechanically coupled to a T-Motor electric generator and supplying a regulated 48 V DC bus. The system is capable of delivering approximately 3 kW of continuous electrical power, with peak values reaching 3.5 kW. Jet-A and three n-octanol/Jet-A blends containing 10%, 20%, and 30% n-octanol by volume, denoted O10, O20, and O30, respectively, were tested under four operating regimes ranging from idle to 2500 W electrical load. Exhaust gas temperature, carbon monoxide, sulfur dioxide, nitrogen oxides, electrical output, and near-field pollutant dispersion were evaluated. The results show that n-octanol addition affects engine behavior in a strongly load-dependent manner. At idle, the O10 blend reduced CO concentration from approximately 2520 ppm for Jet-A to approximately 2270 ppm, corresponding to a reduction of about 9.9%. At the same operating condition, O10 reduced exhaust gas temperature from approximately 498.3 °C to 463.2 °C, while O20 and O30 produced stronger cooling effects. At intermediate regimes, the oxygenated molecular structure of n-octanol contributed to lower CO formation in selected cases, indicating improved combustion-completeness behavior. At high load, however, exhaust gas temperatures converged toward or exceeded those of Jet-A, particularly for O30, showing that higher octanol fractions may introduce additional thermal constraints. Among the tested fuels, O10, corresponding to 10% n-octanol by volume, provided the most balanced behavior across the investigated operating range, from idle to 2500 W electrical load. The dispersion measurements performed at 30 m from the source further showed that ambient pollutant concentrations are strongly influenced by wind speed, wind direction, and plume transport. These findings support moderate n-octanol blending as a promising transitional strategy for small-scale hybrid UAV propulsion systems, while highlighting the need for future repeated testing, direct fuel-flow measurement, and numerical dispersion modeling. Full article

Against the backdrop of the accelerating global low-carbon transition, the mismatch between intermittent renewable energy generation and inflexible energy demand has become a major bottleneck for sustainable energy development. To address this challenge, this paper proposes an AI-driven 4N4F (Four Networks and Four Flows) multi-network convergence system. The paper first clarifies the conceptual connotation and theoretical foundation of 4N4F integration and then systematically constructs an overall technical architecture built on three core pillars: intelligent connected electric vehicles as mobile energy nodes, intelligent networked building-integrated photovoltaics as stationary energy carriers, and an integrated energy management system as the global coordination hub. The effectiveness of the proposed framework is examined through representative engineering cases covering commercial buildings, hybrid energy storage, off-grid microgrids, and sustainable campuses. The results demonstrate that the 4N4F system breaks down traditional silos among energy, information, transportation, and human networks, significantly improving renewable energy utilization and overall energy efficiency. These findings highlight the framework’s scientific contribution as a new technical paradigm for modern energy-system construction and sustainable development. Full article

The dynamic distribution of braking forces between front and rear axles in electric commercial vehicles represents a critical multi-objective optimization challenge requiring simultaneous satisfaction of regulatory safety compliance, regenerative energy recovery, thermal stability, and actuator coordination under varying load and road conditions. This paper addresses this challenge through the development and experimental validation of an integrated adaptive brake force distribution strategy combining model predictive control (MPC) with Particle Swarm Optimization (PSO) within a unified framework that ensures compliance with ECE Regulation No. 13. A comprehensive experimental test bench was designed and instrumented, integrating three independent braking mechanisms: magnetic brakes with front and rear torque coefficients of 4.73 N·m/A and 3.65 N·m/A, respectively; an eddy current retarder with coefficient $k_0=$ 2.220 × 10⁻⁴ N·m·s/(A²·rad), producing braking torque that is quadratic in excitation current and linear in rotor speed; a regenerative braking system with 82–90% efficiency; and a switchable magnetic clutch for FWD/4WD operation. The MPC controller was formulated with a prediction horizon $N_p$ = 20, control horizon $N_c$ = 5, and sampling time $T_s$ = 20 ms. PSO was employed for systematic tuning of MPC weights using 30 particles over 50 iterations with cognitive and social coefficients $c_1$ = $c_2$ = 2.0 and linearly decreasing inertia from 0.8 to 0.4. A vehicle state estimation module using Kalman Filtering was developed for real-time estimation of vehicle mass (<3% error), road slope (<0.3% error), and road friction coefficient (<5% error). Experimental validation across eight comprehensive test scenarios demonstrates that the PSO-optimized MPC controller achieves 43% reduction in front RMSE (from 2.65 Nm to 1.52 Nm), 44% reduction in rear RMSE (from 0.78 Nm to 0.44 Nm), 100% ECE R13 compliance (improved from 67.5%), 57% settling time improvement (from 4.2 s to 1.8 s), 92% overshoot reduction (from 67% to 5%), and average recovered energy improvement from 3.51 kJ to 4.04 kJ. The proposed framework provides a comprehensive solution for next-generation electric commercial vehicle brake management systems. Full article

This study investigates the aerodynamics and energy balance of a roof-mounted photovoltaic system on a vehicle’s energy consumption at different driving speeds. A 3D vehicle model, both with and without the PV system, was analyzed using aerodynamic simulation to obtain the data required for the evaluation. The results quantify the increase in aerodynamic drag and the corresponding rise in energy consumption caused by the PV system installation and determine the necessary reduction in cruising speed for the PV-equipped vehicle to match the energy consumption of the baseline configuration. The findings indicate that at low urban speeds, the energy generated by the PV system will add extra mileage, while at higher speeds, a moderate reduction in cruising speed can significantly mitigate the aerodynamic penalty. These insights help define the operational conditions under which roof-mounted photovoltaic systems provide a net benefit for electric or hybrid vehicles. Full article

Unmanned aerial vehicles (UAVs) are increasingly considered as enabling technologies for last-mile delivery, emergency medical response, and smart-city applications. However, drone-based logistics, emergency communication, sensing activities, and future aerial mobility are often addressed as separate research domains. This article proposes a four-dimensional planning framework for drone-enabled mobility, integrating goods, information, sensing, and human mobility within a unified conceptual structure. The framework is developed through a literature-informed conceptual analysis and previous applied research experiences related to drone-assisted logistics and emergency communication. Goods mobility includes parcel delivery, medical logistics, emergency supply transport, and hybrid operational models involving trucks, public transport, depots, and micro-hubs. Information mobility refers to the use of drones as mobile communication tools for emergency warnings, citizen interaction, drone-to-infrastructure communication, and infomobility services. Sensing mobility concerns traffic monitoring, environmental observation, disaster mapping, crowd monitoring, and infrastructure inspection. Human mobility is considered as an emerging extension related to urban air mobility (UAM), electric vertical take-off and landing (eVTOL) systems, and low-altitude aerial corridors. Cross-cutting issues such as energy autonomy, solar-assisted drones, multimodal integration, safety, communication, regulation, sustainability, and public acceptance are discussed. The proposed framework provides a structured basis for assessing drones as components of sustainable, resilient, and multimodal mobility systems. Full article

The increasing complexity of automotive production systems, driven by mass customization, the transition from internal combustion engine vehicles to electric vehicles, competitive markets, and cost pressure, has intensified the need for advanced optimization across production and intralogistics planning. Optimization refers to the process of determining the best feasible solution to a decision problem according to a defined objective function, subject to given constraints. Whereas most reviews focus on individual problem classes, this systematic literature review adopts an integrated production and intralogistics perspective. Following the PRISMA statement, four research questions on problem types, their classification, solution methods, and trends are addressed by searching four databases (Web of Science, IEEE Xplore, ACM Digital Library, and Science Direct), yielding 194 publications from 1983 to 2025. The analysis shows that production planning, scheduling, resource management, and uncertainty and robustness are the dominant problem types, while metaheuristics, exact methods, and simulation are the most common, frequently hybridized solution methods. Publication activity has risen sharply, with 73.7% of studies appearing since 2019 and material feeding, sustainability, human–robot collaboration, and machine learning showing increased recent publication activity. A taxonomy classifying optimization problems among five dimensions, problem type, decision level, objectives, solution methodology, and real-data usage is proposed to guide researchers and practitioners towards an integrated, industrially deployed optimization. Full article

The energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs) plays an important role in determining both hydrogen economy and the state-of-charge (SOC) regulation and prevention of over-charge/discharge. Existing fuzzy logic-based EMS typically employs a single fuzzy logic controller with fixed SOC thresholds, which struggles to adapt to complex and varying driving conditions. To address this limitation, this paper proposes a dual-fuzzy logic control strategy comprising a primary fuzzy logic controller (FLC1) and a secondary fuzzy logic controller (FLC2), which operate in coordination to achieve refined power distribution through ΔSOC adjustment, defined as the difference between the state of charge (SOC) of a power battery and its ideal SOC (ISOC). A series of systematic simulations are conducted under different ISOC coefficients (0.45, 0.50, 0.55, 0.60, and 0.65), and the results are compared with those obtained from the switch-fuzzy control strategy. The findings indicate that an ISOC of 0.55 yields the optimal comprehensive performance. Building on this, an extremum-seeking control (ESC) algorithm is introduced to perform online adaptive optimization of the ISOC. A comprehensive cost function that integrates hydrogen consumption, power loss, and SOC deviation is constructed to dynamically adjust the ISOC. Simulation verification is conducted on the MATLAB/Simulink R2024b and AVL CRUISE co-simulation platform under the New European Driving Cycle (NEDC). Results demonstrate that the ESC adaptive strategy achieves hydrogen consumption reductions of 10.0%, 34.6%, and 45.7% under initial SOC conditions of 35%, 75%, and 85%, respectively, compared with the switch-fuzzy control. Furthermore, it delivers an additional 1.21% hydrogen saving over the optimal dual-fuzzy control at an initial SOC of 75%. Notably, the ISOC update in the proposed strategy does not require prior knowledge of future driving cycles, and the optimization itself is performed online. However, the ESC hyperparameters (perturbation amplitude, frequency, filter time constants, integrator gain) and the cost function weights are pre-calibrated offline based on typical operating conditions, and the real-time cost evaluation relies on component efficiency signals derived from the vehicle model, providing a practical and intelligent upgrade pathway for fuzzy logic-based EMS in FCHEV applications. Full article

Aimed at addressing the issue of frequent short-duration ICE start–stop events in P2-configuration plug-in hybrid electric vehicles (PHEVs) employing conventional instantaneous optimization-based Equivalent Consumption Minimization Strategy (ECMS)—and the resulting deterioration of vehicle smoothness, NVH performance, fuel economy, and emission performance—this paper proposes a novel energy management strategy, designated ECMS-ISS, which integrates instantaneous optimization with an engine unnecessary start suppression algorithm. A multilayer perceptron (MLP) neural network is first constructed as an online identifier to recognize high-frequency intervals of frequent start–stop events in real time. A dedicated penalty function is then embedded within these identified intervals, with the penalty intensity adaptively adjusted according to the accumulated count of short-duration start–stop events, enabling zoned and targeted intervention without affecting engine torque output during normal operating intervals. Simulation results under NEDC and WLTC driving cycles demonstrate that, compared with the conventional A-ECMS, ECMS-ISS reduces engine start–stop events by 35.48% and 32.31%, respectively, and reduces comprehensive fuel consumption by 2.13% and 5.40%, while significantly decreasing CO, NOx, and HC emissions. Compared with RB-EMS, ECMS-ISS also exhibits superior fuel economy and emission reductions, with the final SOC maintained within a reasonable range throughout. The proposed strategy demonstrates distinct advantages in reconciling multiple objectives, including start–stop rationality, fuel economy, emission performance, and battery health, thereby providing a practical and adaptive solution to the frequent engine start–stop problem in P2-configuration PHEVs. Full article