Search Results (8,861)

In Australia’s National Electricity Market (NEM), community-scale battery energy storage systems (BESS) operate under five-minute price volatility and frequent negative pricing. However, unmeasured internal states and degradation processes constrain the effectiveness of rule-based and simplified optimisation methods for real-time arbitrage. To address these challenges, this study proposes a data-driven digital-twin framework for real-time management of a 1 MW/4 MWh grid-connected community BESS. The framework integrates a control-oriented single-particle model (SPM), an Unscented Kalman Filter (UKF)-based estimation layer for state-of-charge (SOC), state-of-health (SOH) and internal-state estimation and a degradation-aware nonlinear model predictive control (NMPC) strategy. Within this architecture, the SPM provides an interpretable electrochemical representation, the estimation layer reconstructs internal states from measurable signals, and the NMPC performs five-minute rolling arbitrage subject to voltage, power, and SOC constraints while accounting for ageing-related costs and ramp penalties. Simulation case studies based on high-volatility daily price profiles from four NEM regions indicate that the proposed framework can coordinate arbitrage-oriented dispatch, constraint-aware operation, and degradation-related cost consideration under the tested conditions. These results suggest the potential of the SPM–UKF–NMPC digital-twin architecture for supporting real-time community-scale BESS management, while further validation under forecast uncertainty and hardware or field conditions remains necessary. Full article

This paper develops a real-time rolling-horizon optimization framework based on semidefinite programming (SDP) for vehicle-to-grid (V2G)-enabled electric vehicle (EV) fleets in distribution networks. The model coordinates time-varying EV availability, departure energy requirements, and distribution-network operating constraints under alternating-current (AC) power flow. The objective integrates voltage-dependent network loss cost, load-dependent EV energy transaction cost, and throughput-based battery degradation cost, while asymmetric charging/discharging efficiencies, EV implementation errors, and load forecast errors are also considered. To address the nonconvexity caused by AC power-flow equations and voltage-dependent losses, Hermitian lifting is used to reformulate the problem into a rank-constrained SDP model, followed by a convex SDP relaxation. Numerical studies on IEEE 33-bus and IEEE 69-bus systems show that the proposed rolling SDP method reduces EV-induced load peaks, improves load-smoothing performance, satisfies network and EV-side constraints, and yields numerically rank-one solutions in the tested cases. Further tests on time-slot lengths, look-ahead horizons, EV penetration levels, benchmark methods, EV implementation errors, and load forecast errors further verify the effectiveness and practical robustness of the proposed framework. Full article

The increasing adoption of electric vehicles (EVs) demands highly efficient bidirectional DC–DC converters capable of seamless energy transfer between the grid and vehicle batteries. This paper introduces a Fixed-Frequency Dual-Active-Bridge (DAB) resonant converter featuring four degrees of freedom, achieved through a combination of triple phase-shift (TPS) modulation and a current-controlled variable inductor (VI). The proposed control strategy aims to minimize conduction and switching losses by simultaneously managing reactive power, RMS current, and soft-switching conditions across wide variations in voltage and power. Unlike conventional phase-shift or variable-frequency modulations, the fixed-frequency operation maintains full zero-voltage switching (ZVS) for the two bridges, and zero-current switching (ZCS) in the bridge that is receiving energy, enhancing overall system reliability and control simplicity. The proposed converter is validated through simulations and experimental results from a SiC MOSFET-based 14 kW prototype operating at 122 kHz, demonstrating peak efficiencies above 97% under both charging and discharging modes. The experimental results confirm that the proposed DAB topology and modulation scheme significantly improve efficiency and controllability, making it a promising solution for next-generation on-board chargers and vehicle-to-grid (V2G) applications. Full article

Effective thermal management is crucial for the performance, thermal safety, and lifespan of lithium-ion batteries in electric vehicles (EVs). Thermal management strategies are essential for preventing overheating, thermal imbalance, and the associated risk of thermal runaway. Nanofluids are emerging and attracting considerable attention as potential coolants for high-power energy storage and electronics systems. This review updates and summarizes the most recent advances in nanofluid-based cooling strategies for battery thermal management systems (BTMSs) over the past five years, emphasizing their implications for battery thermal safety. Three main nanofluid-based cooling strategies have been evaluated in depth, including nanofluid-based indirect liquid cooling, nanoparticle-enhanced PCM cooling, and nanofluid-based heat pipe cooling. Various nanofluid formulations, including mono, hybrid, and ternary nanofluids, have been considered and evaluated for their heat dissipation under high charge/discharge and abuse-relevant conditions. Thermal and hydraulic performance characteristics, including maximum temperature, maximum temperature difference, and pressure drop, have been comprehensively evaluated for different nanofluid-based cooling strategies. The findings demonstrated that nanofluids significantly improved heat transfer rates and enhanced temperature control efficiency. In particular, hybrid and ternary nanofluids exhibit superior thermal performance and effectively suppress the escalation of safety-critical temperatures. Beyond summarizing cooling performance, this review further discusses the role of nanofluid-based cooling strategies as functional thermal-control layers within intelligent BTMS architectures. Particular attention is given to their compatibility with sensing networks, BMS-/VCU-level supervisory control, predictive thermal models, actuator responsiveness, fault-warning algorithms, and long-term reliability under realistic driving and fast charging conditions. Therefore, this review provides architecture-oriented insights for developing safe, energy-efficient, and control-ready BTMSs for next-generation high-power and connected EVs. Full article

The participation of battery swapping station (BSS) clusters in grid regulation is significantly constrained by the spatio-temporal uncertainty and climate sensitivity of electric vehicle (EV) demand. To address these issues, this paper proposes an aggregated scheduling method that integrates demand forecasting and rolling optimization. First, a demand forecasting model is established by considering seasonal climate and users’ range anxiety. On this basis, a “day-ahead bidding and intra-day tracking” two-stage scheduling framework is constructed. In the day-ahead stage, the optimal bidding power of the cluster is determined for minimizing the overall cluster cost. In the intra-day stage, taking the bidding power as the tracking index, the demand distribution scheme and station charging/discharging strategies are synergistically optimized to minimize the operational costs. Furthermore, for real-time EV swapping requests, suitable BSS nodes are recommended based on the distribution scheme. To address the stochasticity of user rejection, rolling optimization is applied for real-time adjustments, ensuring reliable grid response and service quality. Finally, a case study using real operational data verifies the effectiveness of the proposed model. Full article

Microwave wireless power transfer (MWPT) is a promising technology for powering dedicated industrial Internet of Things (IoT) devices, enabling battery-free operation. However, in realistic MWPT deployments, the received RF signals fluctuate drastically due to varying transmission distances and multipath fading. Additionally, the equivalent impedance of sensor nodes varies significantly during duty cycles, shifting between a low-resistance active state and a high-resistance sleep state. Consequently, maintaining high rectification efficiency under these dynamic conditions remains a critical challenge. This paper proposes a high-efficiency rectifier with a wide input power and load range based on the suppression of second and third harmonics. The rectifier adopts a dual-diode parallel configuration. By leveraging the impedance compensation characteristics of two short-circuited stubs with distinct electrical lengths, it simultaneously achieves fundamental-frequency impedance matching and harmonic suppression without the need for an additional matching network. Validated through theoretical derivation, simulation analysis, and physical prototype testing, the proposed 2.45 GHz rectifier realizes high-efficiency rectification over a wide dynamic range. Experimental results demonstrate that the power dynamic range reaches 10 dB when the rectification efficiency exceeds 70%, and extends to 17 dB when the efficiency is above 60%. Furthermore, the rectification efficiency is insensitive to load variations (100–1200 Ω), making it highly suitable for powering wireless sensor nodes with varying operating modes in complex electromagnetic environments. Full article

Unmanned aerial vehicle (UAV) routing under realistic operational conditions requires simultaneous consideration of distance- and payload-dependent energy consumption, limited battery capacity, and multi-trip mission feasibility—factors that are rarely integrated into a unified, reproducible benchmarking framework. This study proposes an energy-aware, multi-trip UAV routing model for single-warehouse cargo delivery operations, in which total energy consumption is minimized through a second-degree polynomial power function derived from empirical motor thrust–power data of a theoretically designed quadrotor UAV with a maximum payload capacity and a usable battery capacity. Euclidean service locations and loads are generated randomly within a continuous operational domain to reflect spatial uncertainty, and a split-based decoding mechanism enforces battery feasibility constraints throughout the route. Twenty-six heuristic and metaheuristic algorithms sourced from the recent UAV routing literature are implemented within a standardized MATLAB benchmarking environment and evaluated on TSPLIB instances (Berlin52, kroA100), as well as randomly generated instances with different numbers of delivery locations. A refined subset of eight representative algorithms is subjected to comprehensive scalability analysis under both distance- and energy-minimization objectives, separately. The findings provide evidence-based guidelines for algorithm selection across offline planning and real-time UAV routing scenarios, and establish a transparent, reproducible benchmark baseline for energy-constrained single-UAV operations. Full article

Electric vehicles (EVs) relying solely on lithium-ion (Li-ion) batteries face limitations related to range, mass, charging time, and battery downsizing. This study develops a dynamic system-level modeling framework for integrating an aluminum–air (Al–air) battery with a Li-ion traction battery within a MATLAB/Simulink electric vehicle platform. Two integration strategies were evaluated: (i) Al–air operation as a range extender activated through SOC-based control logic, and (ii) Al–air operation as an auxiliary power unit supplying non-traction loads. The Al–air subsystem was implemented using an experimentally informed polarization-based model coupled with aluminum consumption tracking and DC–DC converter integration. Vehicle performance was evaluated under UDDS, HWFET, WLTP, and FTP-75 drive cycles. Results show that coupling a 24.6 kWh Al–air pack with a downsized 20.3 kWh Li-ion pack enabled driving ranges of 379 km (UDDS), 523 km (HWFET), and 450 km (WLTP), exceeding the baseline full-capacity Li-ion configuration while reducing total battery-system mass by more than 50%. When operated as an auxiliary power unit under a constant 3 kW auxiliary load, the Al–air system increased the vehicle range by 44–96 km depending on the drive cycle. The results demonstrate the feasibility of Al–air-assisted dual-energy-storage architectures for extending the EV range while reducing dependence on large Li-ion battery packs. Full article

Standalone photovoltaic DC energy systems must maintain bus voltage stability without grid support; however, abrupt load variations can cause a DC-bus voltage drop, reducing system reliability and disturbing connected equipment. Although battery-based energy storage is effective for long-duration power balancing, its response to instantaneous disturbances can be limited. This study proposes an energy stability strategy using supercapacitor-based ride-through control and required capacity sizing for fast DC-bus voltage support. The proposed controller continuously monitors the DC-bus voltage and, when a voltage drop is detected, immediately triggers supercapacitor discharge to compensate for the power deficit until the bus recovers. In addition, a design formulation is derived to estimate the required compensation energy, ride-through time, and minimum capacitance based on the expected power deficit, allowable DC-bus voltage drop, and initial supercapacitor voltage. Simulation results under step changes in load resistance show that the supercapacitor sized by the proposed method maintains the DC-bus voltage close to its reference value within the specified limit. Hardware experiments further validate the ride-through operation and show good agreement between the predicted and measured compensation times. Full article

An increasing penetration of distributed energy resources and electrification-driven peak demand pose significant challenges to distribution networks, often resulting in voltage violations and congestion. This paper presents a multi-stage optimization framework that enables battery storage unit (BSU) to act as a flexible non-wire alternative to traditional grid expansions conducted by Distribution System Operators (DSO), but also helpful for Transmission System Operators (TSO). The proposed method integrates a mixed-integer planning model with a quadratically constrained, second-order-cone–relaxed, AC optimal power flow to determine the optimal siting and sizing of battery storage. Representative operating days are obtained through clustering, while the operational optimization model evaluates battery participation in energy and reserve markets under network constraints. The value of flexibility the DSO procures from an independently-owned battery storage unit is determined as the opportunity cost of providing this flexibility as opposed to taking part in the fast reserves and day-ahead energy markets. The results obtained offer valuable information when weighing the decision between network expansion and alternative strategies and determine the price of flexibility that the DSO can offer to an independently owned storage unit. The results confirm that battery storage can defer network investments while providing transparent and economically justified flexibility remuneration. The proposed framework is implemented sequentially, with strong coupling between planning and operational stages through physical constraints and economic signals. Full article

To meet climate goals, the automotive industry is transitioning to electromobility, reshaping vehicle model variants, market composition and therefore influencing purchasing decisions. To cover the full range of possible vehicle models for the German passenger vehicle market, a machine learning-based manufacturer agent was developed, incorporating a comprehensive technology database and historical vehicle data. Over 3000 new BEV models were generated and evaluated for possible year of market entry. Relevant models were integrated into the VECTOR21 vehicle technology scenario model to assess their market potential against competing drivetrains. The scenario results for Germany show that LFP vehicles can capture more than 18% overall market share in 2030, while Ni-rich cells remain competitive in long-range variants with up to 53% market potential by 2035. On the other hand, BEVs powered by sodium-ion batteries could reach up to 9% market potential by 2030, potentially exceeding 17% if cell prices fall below 50 EUR/kWh. However, sensitivity analysis reveals So-Ion market potential is highly sensitive to model availability, dropping to 6% or 2% in constrained scenarios, primarily replaced by LFP variants. These findings suggest that alongside cost reductions, sufficient model availability can also play a significant role in realizing the market potential of next-generation battery technologies. Full article

Rural regions of Uzbekistan experience continuing issues of energy access because of poor grid networks and variable renewable sources. The solution is small-scale wind turbines and energy storage. But the wind speeds and load demand are variable, and thus this solution needs intelligent control systems to perform its best. This paper is an attempt to design an adaptive neuro-fuzzy inference system (ANFIS) controller to control a small wind power system with a battery storage unit. The controller will be intelligent to control the flow of power between the wind turbine, battery, and local loads. A model of MATLAB/Simulink is created to simulate the reaction of the system to various wind and load conditions. The simulation results indicate that the ANFIS controller improves voltage regulation, reduces power fluctuations, and enhances battery charge–discharge performance compared to the conventional PI controller. Environmental variability is effectively responded to by the system, making it more reliable and energy-efficient. ANFIS control and wind–battery microgrid integration provides a feasible and expandable off-grid electrification solution to remote areas. This strategy promotes the renewable energy ambitions of Uzbekistan and offers an example of smart microgrid implementation in other resource-limited rural areas. The next steps would be towards practical applications and hardware verification. Full article

Battery-powered Internet of Things (IoT) sensor nodes for environmental monitoring face strict energy constraints, particularly when employing high-consumption sensors such as particulate matter sensors or gas analyzers. Extending operational lifetime without sacrificing measurement reliability remains a key challenge for large-scale air-quality monitoring deployments. This paper proposes a digital-twin-assisted adaptive sensing algorithm for reducing energy consumption by dynamically optimizing sensor usage for Indoor Air Quality (IAQ) monitoring system. The system consists of distributed sensing nodes and a central station that maintains digital twins to evaluate candidate sensing strategies based on historical data and environmental patterns. Strategies are assessed in terms of energy consumption and measurement fidelity and deployed only when a measurable improvement is achieved. The approach is evaluated across mobile and stationary sensor configurations used for monitoring indoor air quality in university laboratories while educational and research activities are carried out. For stationary nodes, clustering-based scheduling reduces the activation of high-power sensors, while for mobile nodes, variation-based triggering exploits correlations between equivalent and reference CO₂ measurements to limit energy-intensive sensing. Results demonstrate energy savings of up to approximately 70% while maintaining acceptable measurement fidelity. The findings show that reduced sensing can be used for system initialization, while digital twin evaluation enables reliable transition to adaptive sensing under suitable conditions. Full article

Thermal management remains a key challenge for lithium-ion batteries in electric vehicles, especially under transient driving and charging conditions. This study develops a coupled thermo-hydraulic model for a liquid-cooled battery thermal management system and uses it to compare four coolants with different thermophysical properties: water, ethylene glycol–water, propylene glycol–water, and Jet-A aviation fuel. Unlike studies that focus mainly on temperature reduction, the present work evaluates battery temperature, hydraulic pump power, and cooling load/heat rejection demand within the same framework. The coolants are tested under the FTP-75 driving cycle and a high-rate charging case while pump speed is varied between 1500 and 4500 rpm. Water provides the strongest cooling performance, reducing the battery temperature during FTP-75 from about 30 °C to 21.2 °C at 1500 rpm and 20.6–20.8 °C at 4500 rpm. During charging, water maintains the battery temperature near 23 °C at 1500 rpm, whereas ethylene glycol–water and Jet-A reach about 46–47 °C. Increasing pump speed improves thermal regulation, particularly for weaker-performing coolants, but it also increases auxiliary demand; for example, the RMS pump power of water during charging rises from 0.039 to 0.735 kW. Overall, the results show that coolant selection in liquid-cooled BTMS requires a balanced assessment of heat removal capability, pumping demand, and heat rejection requirements. Full article

Real-time energy management for large electric vehicle (EV) clusters requires both fast aggregate flexibility estimation and executable per-vehicle dispatch. Classical LP/MILP/MPC formulations provide strong feasibility and optimality guarantees when the model is fully specified, but their online solve time increases rapidly with cluster size; learning-based methods are fast but often rely on soft constraint penalties or external feasibility repair. We propose the Physics-Constrained Mamba-3 MIMO Aggregator (PC-M3), an amortized, constraint-aware sequence model that integrates a MIMO Mamba backbone, a history-dependent differentiable projection, a sparse routing layer, and an aggregation–disaggregation consistency loop, scaling AI-EMS from a single battery to ten-thousand-vehicle clusters in one forward pass. PC-M3 assigns every EV to one channel of a multi-input multi-output (MIMO) state-space recurrence and embeds the per-vehicle state-of-charge, power and energy constraints as a differentiable in-loop projection, jointly producing the cluster-level flexibility envelope and the per-vehicle charging trajectory. A sparse Routing-Mamba mixture-of-experts layer adaptively allocates capacity to behaviourally distinct sub-populations without supervised labels, and a consistency-trained aggregation–disaggregation loop binds the predicted envelope to the executed dispatch, forming a digital-twin-style predictive EMS pipeline that couples cluster dispatch with per-vehicle SoC evolution. On a single NVIDIA A100, PC-M3 sustains 0.34 s inference for 10,000 EVs over a 24-h horizon, about 18× faster than an Informer baseline and 2.4× faster than PowerMamba. Evaluated on the open ACN-Data and ElaadNL workplace and public charging corpora and on a 10,000-vehicle NREL dsgrid-TEMPO 2030 stress test, PC-M3 reduces the normalised envelope Hausdorff distance from 9.7% (PowerMamba) to 3.4%, cuts closed-loop cluster tracking RMSE from 1.45 MW (model predictive control) to 0.82 MW, and maintains zero observed feasibility violations with respect to the specified or imputed per-vehicle polytopes on every evaluated session. The framework provides a scalable, predictive, constraint-aware AI-EMS for V2G/G2V virtual-power-plant operation of large EV fleets. Full article