Search Results (7,444)

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

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

The energy consumed by thermal management systems strongly affects the driving range of battery electric vehicles. In this study, we develop an integrated control strategy that couples the Sparrow Search Algorithm (SSA) with Nonlinear Model Predictive Control (NMPC) to simultaneously reduce energy consumption and satisfy cabin comfort and battery safety requirements. We construct a multiloop coupled, heat pump-based integrated thermal management model, including a compressor, heat exchangers, expansion valves, and an electro-thermal battery sub-model. Bench and vehicle-level tests confirm that the model predicts the refrigerant mass flow rate and heating capacity with mean relative errors of 4.76% and 4.30%, respectively. The SSA is used to tune the NMPC weighting parameters offline, minimizing the mean absolute errors of the cabin temperature, battery temperature, and total system energy consumption. The resulting SSA-NMPC strategy is evaluated under NEDC and CLTC-P driving cycles. Under the investigated NEDC-based high-load assessment with representative operating conditions, the proposed strategy limits the cabin temperature overshoot to 0.35 °C and battery temperature fluctuation to 0.26 °C, while achieving a 6.31% energy saving under high-speed cruising. The proposed framework focuses on cabin and battery thermal regulation and considers motor waste heat recovery. These results demonstrate that the SSA-NMPC approach can improve thermal management performance under the investigated operating conditions. Full article

Wireless sensor networks (WSNs) deployed for seismic monitoring must sustain long-term operation under strict energy constraints, where premature node failure degrades spatial coverage and detection reliability. This paper presents a safety-constrained reinforcement learning framework for transmission scheduling in energy-harvesting seismic WSNs. The proposed approach integrates Proximal Policy Optimisation (PPO) with action masking and a runtime guard-layer safety filter that enforces battery-preservation and load-balancing constraints without retraining. The guard layer intercepts policy actions and substitutes safe alternatives when constraint violations are detected, using a scoring function that combines battery headroom with network-wide load equity. Experiments across three network scales (10, 15, and 30 nodes) with solar energy harvesting demonstrate that the guard-enhanced PPO achieves 99.46% transmission success at 30 nodes while maintaining 66.47% node survival—a 58.3% improvement in survival over the highest-reward baseline (Closest) at the cost of only a 6.2% reduction in cumulative reward. Crucially, the guard-enhanced policy outperforms the unconstrained PPO baseline simultaneously on cumulative reward ($+11.4\%$), transmission success ($+0.8$ pp), and node survival ($+15.4\%$), demonstrating that hard safety constraints, when properly aligned with the system’s energy model, provide both performance and safety gains rather than a fundamental trade-off. Sensitivity analysis across event rates ($p_{\mathrm{event}}=0.5$ and $0.9$) confirms that the guard layer’s advantage persists under both moderate and extreme monitoring conditions. Analysis across scales reveals distinct operational regimes: at 10 nodes, heuristic baselines are near-optimal; at 30 nodes, learned policies dominate, and safety filtering becomes critical for sustained operation. Full article

The rapid expansion of Unmanned Aircraft System (UAS) operations has created an urgent need for scalable strategic conflict resolution methods within the U-space framework. When requested 4D flight plans overlap with previously authorised ones, the Flight Authorisation Service (FAS) denies the request, and can provide the UAS operator with an alternative route, free of conflict. This work introduces a Machine Learning-based tool designed to support this process, which consists of three sequential phases. First, an Octree spatial partitioning technique is proposed, discretising the airspace, further identifying the previously occupied cells and visualising the occupied airspace, so that the UAS operator can manually find an alternative route. Then, the widely known A* pathfinding algorithm is implemented in this discretized airspace, allowing the shortest or most optimal conflict-free alternative route. Finally, the methodology integrates a Machine Learning (Reinforcement Learning) model, created from scratch and trained with realistic flight trajectories from a PX4 Simulator, to further optimise flight paths, explicitly accounting for operational constraints such as distance and battery consumption. In this work, both methods are compared, addressing traditional algorithms limitations with Machine Learning (ML) techniques, showing that a near-optimal behaviour can be achieved with the ML approach, at a fraction of the computation time needed. 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

To address the issue of thermal runaway in ternary lithium-ion batteries under overcharging conditions, this paper establishes a multi-physics simulation model based on electrochemical–thermal coupling theory to systematically investigate the thermal behavior and runaway mechanisms of the battery. A P2D electrochemical model and the Bernardi heat generation model were combined to construct an electrochemical–thermal coupling model suitable for overcharging conditions. Simulation results indicate that under normal charging conditions, the battery temperature rise is small and uniformly distributed; however, under overcharging conditions, side reactions significantly intensify, leading to a rapid increase in heat generation. The battery temperature exhibits a distinct inflection point and rises rapidly, displaying typical thermal runaway characteristics. Charging rate and ambient temperature have a significant impact on the thermal runaway process; both high charging rates and high ambient temperatures accelerate heat accumulation and reduce battery thermal safety. The study demonstrates that the established model effectively reveals the evolution of thermal runaway in overcharged ternary lithium-ion batteries, providing a theoretical basis for battery thermal management design and safety early warning systems. Full article

Private cloud object stores provide infrastructure isolation but leave application-layer data exposed to insider threats and compromised credentials. This paper presents an engineering integration of an Add-Rotate-XOR (ARX) block cipher and multi-bit Least Significant Bit (LSB) steganography into an end-to-end pipeline for private MinIO object storage. The cipher, KREA v2, is a SPECK-64/128 derived ARX construction with three application-driven choices: CRC32 key whitening, byte-aligned rotations ($\alpha =7$, $\beta =2$), and deterministic CTR-mode nonces. Mixed Integer Linear Programming (MILP) trail analysis matches SPECK-64/128’s minimum-trail weights through rounds 1–4. KREA v2 ciphertext meets standard keystream-quality preconditions (NIST SP 800-22 battery, 49.98% mean avalanche, Shannon entropy 7.9992–7.9998 bits/byte across realistic XML, JSON, video, and HTTP/2 payloads). Modified LSB (MLSB) embeds 3 bits per RGB channel with an XOR watermark at 37–38 dB Peak Signal-to-Noise Ratio (PSNR), providing $3\times$ standard-LSB capacity. Steganalysis uses chi-square and RS detectors plus a Convolutional Neural Network (CNN) detector (Yedroudj-Net) trained on 8000 BOSSBase-1.01 cover/stego pairs; CNN area under the ROC curve is ≥0.999 against the watermarked variant. The MinIO pipeline runs at 355.1 ms (68.6% network I/O) with 100% message fidelity. The XOR watermark increases RS detectability above 75% capacity; a 200-image ablation cuts median RS detection (0.289 to 0.000) and mean (0.342 to 0.130) in a sparse-keystream variant, prioritised for follow-on full-scale evaluation. The architecture is offered as a documented engineering integration with explicit security caveats and threat-model boundaries, not as a production-hardened cryptographic primitive. 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

Thermal management is critical for the safety, performance, and life cycle of lithium-ion (Li-ion) batteries. This study aims to determine the optimum settings and contribution levels of key parameters affecting the operating temperature of a three-dimensional (3D) thermal model of a cylindrical Li-ion battery. A Taguchi L9 orthogonal array was designed with four: (A) base fluid and (B) Al₂O₃volume fraction (Φ-Al₂O₃) of the nanofluid coolant, (C) battery–battery distance, and (D) inlet temperature (T_inlet), each varied on 3-level control factors. To minimize the maximum battery temperature (T_max), the “smaller-is-better” signal-to-noise (S/N) ratio approach and Analysis of Variance (ANOVA) were applied. The S/N analysis and ANOVA revealed that the base fluid (A: 44.96%) and T_inlet (D: 36.00%) were the most dominant factors influencing the T_max. The optimal design identified by the Taguchi method (A3-B3-C3-D1) successfully reduced the T_max to 33.5 °C, a 29.0 °C reduction compared with the initial air-cooled reference model (62.5 °C). Furthermore, the maximum temperature rise during the 2100 s operation was reduced by approximately 62%. This optimal T_max of 33.5 °C was even lower than the best result in the L9 array (35.5 °C), validating the strong predictive capability of the method. Full article