Search Results (2,321)

Machine learning (ML) is becoming a key enabler in building energy management systems (BEMS), yet most existing reviews focus on simulations and fail to reflect the realities of real-world deployment. In response to this limitation, the present work aims to present a systematic review dedicated entirely to experimental, field-tested applications of ML in BEMS, covering systems such as Heating, Ventilation & Air-conditioning (HVAC), Renewable Energy Systems (RES), Energy Storage Systems (ESS), Ground Heat Pumps (GHP), Domestic Hot Water (DHW), Electric Vehicle Charging (EVCS), and Lighting Systems (LS). A total of 73 real-world deployments are analyzed, featuring techniques like Model Predictive Control (MPC), Artificial Neural Networks (ANNs), Reinforcement Learning (RL), Fuzzy Logic Control (FLC), metaheuristics, and hybrid approaches. In order to cover both methodological and practical aspects, and properly identify trends and potential challenges in the field, current review uses a unified framework: On the methodological side, it examines key-attributes such as algorithm design, agent architectures, data requirements, baselines, and performance metrics. From a practical standpoint, the study focuses on building typologies, deployment architectures, zones scalability, climate, location, and experimental duration. In this context, the current effort offers a holistic overview of the scientific landscape, outlining key trends and challenges in real-world machine learning applications for BEMS research. By focusing exclusively on real-world implementations, this study offers an evidence-based understanding of the strengths, limitations, and future potential of ML in building energy control—providing actionable insights for researchers, practitioners, and policymakers working toward smarter, grid-responsive buildings. Findings reveal a maturing field with clear trends: MPC remains the most deployment-ready, ANNs provide efficient forecasting capabilities, RL is gaining traction through safer offline–online learning strategies, FLC offers simplicity and interpretability, and hybrid methods show strong performance in multi-energy setups. Full article

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work. Full article

The article presents the development and results of emission studies conducted in Poland in the context of global real-driving emissions research. Although the European Union has continuously tightened exhaust-emission standards, road transport remains one of the major sources of air pollution. Several research centers in Poland—including Rzeszów University of Technology, Poznan University of Technology, and the Motor Transport Institute—have been conducting on-road emission measurements for many years across a wide spectrum of vehicles: conventional, hybrid (including plug-in hybrids), and fully electric. The findings show that emissions under real-world driving conditions often differ from those obtained in homologation tests, particularly for nitrogen oxides and particulate matter. Ambient temperature, road gradient, and driving phases (urban, rural, motorway) were also identified as influential factors. Polish research centers have developed analytical tools enabling comparison between laboratory and on-road tests and allowing real-driving emissions to be estimated based on chassis-dynamometer data. Studies on plug-in hybrids highlighted that these vehicles remain environmentally beneficial only when regularly charged; otherwise, their emissions can increase sharply. Overall, the research confirms that on-road testing is essential for a reliable evaluation of vehicle performance, and the results can contribute to designing more eco-friendly technologies and improving future emission regulations. Full article

The global transition from internal combustion engines (ICEs) to hybrid (HEVs) and electric vehicles (EVs) is fundamentally reshaping lubricant design requirements, driven by evolving thermal demands, electrical constraints, and material compatibility challenges. Conventional ICE lubricants are primarily formulated to withstand high operating temperatures, mechanical stresses, and combustion-derived contaminants through established additive chemistries such as zinc dialkyldithiophosphate (ZDDP), with thermal stability and wear protection as dominant considerations. In contrast, HEV lubricants must accommodate frequent start–stop operation, pronounced thermal cycling, and fuel dilution while maintaining performance across coupled mechanical and electrical subsystems. EV lubricants represent a paradigm shift, where requirements extend beyond tribological protection to include electrical insulation and conductivity control, thermal management of electric motors and battery systems, and compatibility with copper windings, polymers, elastomers, and advanced coatings, alongside mitigation of noise, vibration, and harshness (NVH). This review critically examines lubricant behavior, formulation strategies, and performance requirements across ICE, HEV, and EV powertrains, with specific emphasis on heat transfer, electrical performance, and lubricant–material interactions, covering mineral, synthetic, and bio-based fluids. Additionally, regulatory drivers, sustainability considerations, and emerging innovations such as nano-additives, multifunctional and smart lubricants, and AI-assisted formulation are discussed. By integrating recent research into industrial practice, this work highlights the increasingly interdisciplinary role of tribology in enabling efficient, durable, and sustainable mobility for next-generation automotive systems. Full article

As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This study presents a multi-objective Whale–Particle Swarm Optimization Algorithm (MOWPSO) for tuning the control parameters of a HESS composed of a lithium-ion battery and a supercapacitor. The proposed full-active configuration with dual bidirectional DC converters enables precise current sharing and independent regulation of energy and power flow. The optimization framework minimizes four objectives: mean battery current amplitude, cumulative aging index, final state-of-charge deviation, and an auxiliary penalty term promoting consistent battery–supercapacitor cooperation. The algorithm operates offline to identify Pareto-optimal controller settings under the Federal Test Procedure 75 cycle, while the selected compromise solution governs real-time current distribution. Robustness is assessed through multi-seed hypervolume analysis, and results demonstrate over 20% reduction in battery aging and approximately 25% increase in effective cycle life compared to battery-only, rule-based and metaheuristic algorithm strategies control. Cross-cycle validation under highway and worldwide driving profiles confirms the controller’s adaptability and stable current-sharing performance without re-tuning. Full article

The increasing electrification of mobility within smart cities has accelerated the need for intelligent energy management strategies that jointly address cost, emissions, and battery health. This study develops a health-aware hybrid reinforcement–predictive energy manager (H-RPEM) designed for photovoltaic–electric vehicle (PV-EV) microgrids. The proposed controller unifies model-based predictive optimisation with adaptive reinforcement learning to achieve both short-term operational efficiency and long-term asset preservation. A comprehensive dataset of solar generation, EV charging behaviour, and stochastic load profiles was employed to train and validate the hybrid control framework under realistic operating conditions. Quantitative results indicate that the proposed H-RPEM controller achieves an 18.7% reduction in total operating cost and a 22.5% decrease in carbon emissions, whilst maintaining the battery state-of-health above 0.95 throughout a 24 h operational cycle. When benchmarked against standard predictive control, the hybrid strategy converges 30–40 episodes faster and delivers a 25% improvement in reward stability, demonstrating enhanced robustness and learning efficiency. The results confirm that H-RPEM achieves robust and balanced performance across economic, environmental, and technical domains, establishing it as a scalable and health-conscious control solution for next-generation smart city microgrids. Full article

The increasing penetration of electric vehicle (EV) fast-charging stations (FCSs) into distribution networks and microgrids poses considerable operational challenges, including voltage deviations, increased power losses, and peak load stress. This work proposes a novel hybrid optimization framework that integrates Lagrangian relaxation (LR) with adaptive sheep flock optimization (ASFO) to address the resource scheduling issues when EVs are penetrated and their impact on net load demand, total cost. Besides the impact of EV uncertainty on energy exchange cost and operational costs, voltage profile deviations were also studied. LR is employed to decompose the original problem and manage complex operational constraints, while ASFO is employed to solve the relaxed subproblems by efficiently exploring the high-dimensional, non-convex solution space. The proposed method is tested on an IEEE 33-bus distribution system with integrated PV and BESS under 24 h dynamic load and renewable scenarios. Results establish that the hybrid LR-ASFO method significantly outperforms conventional methods. Compared to standalone metaheuristics, the proposed framework reduces total cost by 5.6%, improves voltage profile deviations by 2.4%, and minimizes total operational cost by 4.3%. Furthermore, it safeguards constraint feasibility while avoiding premature convergence, thereby accomplishing better global optimality and system reliability. Full article

The switched reluctance motors (SRMs) are an attractive solution for electric vehicles (EVs) and hybrid electric vehicles (HEVs). However, the main drawbacks of SRMs are their highly nonlinear magnetic characteristics, complicated control algorithms, and the inherent torque ripples. This paper presents a simple structure average torque control (ATC) technique with a better ability to reduce torque ripples. Based on the detailed analysis of an inductance profile, this paper introduces a novel current compensation mechanism (CCM) that has the ability to profile the phase current and, hence, reduce the torque ripple. The proposed CCM is meant for the minimum inductance zone (MIZ) to profile the current of the ongoing phase. Over the MIZ, the inductance is independent of the phase current that helps to simplify the deduced mathematical formulations and provides a simple structure ATC with a lower computational burden, making it a feasible solution for real-time implementations and future developments. A series of experimental results are achieved to show the feasibility and effectiveness of the proposed ATC technique. The results show the superior performance of the proposed ATC, providing better torque profiles and reducing the torque ripples with an average value of 30% compared to conventional ATC. Full article

Permanent magnet synchronous machines (PMSMs) are the preferred choice for electric vehicles (EVs), hybrid EVs, and wind turbines because of their high torque density, efficiency, and wide constant-power speed range. Conventional PMSMs rely heavily on rare-earth (RE) permanent magnets like Nd-Fe-B, which offers high remanence and coercivity but comes with high costs, supply chain issues, and environmental concerns. To address these challenges, this paper explores the potential of tetragonal Fe₂Ni₂N, a newly developed RE-free permanent magnet, as a replacement for commercial Nd-Fe-B (N35) in high-performance PMSMs. Fe₂Ni₂N shows a remanent flux density of 1.2 T and coercivity of 0.957 MA/m, closely matching those of commercial N35 magnets. Finite element analysis (FEA) in Ansys Maxwell was performed on both surface-mounted (SPM) and interior-mounted (IPM) PMSMs under EV-representative operating conditions. Results demonstrate that Fe₂Ni₂N-based machines have similar demagnetization resistance, torque, and efficiency to those with N35 magnets, with slight performance advantages at low speeds and nearly identical performance at high speeds. Furthermore, system-level parameters such as DC bus voltage and stator current were analyzed, showing that increased voltage extends the constant torque region while higher current enhances torque output but can slightly reduce efficiency at elevated speeds. These findings confirm that Fe₂Ni₂N is a promising RE-free alternative to Nd-Fe-B for sustainable, high-performance PMSMs. Results show that Fe₂Ni₂N-based machines have similar demagnetization resistance, torque, and efficiency to those with N35 magnets, with slight performance benefits at low speeds and nearly identical results at high speeds. Furthermore, system-level parameters, such as DC bus voltage and stator current, were analyzed. The results show that increased voltage extends the constant-torque region, while higher current enhances torque output but can slightly reduce efficiency at elevated speeds. These findings confirm that Fe₂Ni₂N is a promising RE-free alternative to Nd-Fe-B for sustainable, high-performance PMSMs. Full article

This study presents the design, real-time implementation, and full-scale experimental validation of a rule-based Energy Management Strategy (EMS) for a dual-stack Fuel Cell Hybrid Electric Vehicle (FCHEV) developed on a Jeep Wrangler platform. Unlike previous studies, predominantly focused on simulation-based analysis or single-stack architectures, this work provides comprehensive vehicle-level experimental validation of a deterministic real-time EMS applied to a dual fuel cell system in an SUV-class vehicle. The control algorithm, deployed on a National Instruments CompactRIO embedded controller, ensures deterministic real-time energy distribution and stable hybrid operation under dynamic load conditions. Simulation analysis conducted over eight consecutive WLTC cycles shows that both fuel cell stacks operate predominantly within their optimal efficiency range (25–35 kW), achieving an average DC efficiency of 68% and a hydrogen consumption of 1.35 kg/100 km under idealized conditions. Experimental validation on the Wrangler FCHEV demonstrator yields a hydrogen consumption of 1.67 kg/100 km, corresponding to 1.03 kg/100 km·m² after aerodynamic normalization (Cd·A = 1.624 m²), reflecting real-world operating constraints. The proposed EMS promotes fuel-cell durability by reducing current cycling amplitude and maintaining operation within high-efficiency regions for the majority of the driving cycle. By combining deterministic real-time embedded control with vehicle-level experimental validation, this work strengthens the link between EMS design and practical deployment and provides a scalable reference framework for future hydrogen powertrain control systems. Full article

The booming number of electric vehicles (EVs) and autonomous vehicles is driving the demand for the development of 5G and connected vehicle technologies. However, the design of compact, multi-band vehicular antennas with multiple communication standard support is complex. Traditional experience-based and parameter-sweeping approaches to antenna optimization are often inefficient and limited in scalability, while machine learning-based methods require extensive datasets, which are computationally intensive. This study proposes a microstrip planar Yagi antenna optimized for Sub-6 GHz 5G and cellular vehicle-to-everything (C-V2X) communication. As a way to approach antenna optimization with lower computing cost and less data, a hybrid optimization strategy is presented that combines parametric analysis and curve fitting based data visualization approaches. The proposed antenna exhibits a reflection coefficient of −31.68 dB and −29.36 dB with 700 MHz and 900 MHz bandwidths for frequencies of 3.5 GHz and 5.9 GHz, respectively. Moreover, the proposed antenna exhibits a peak gain of 7.55 dB with a size of 0.44 × 0.64 ${\lambda }^2$, while achieving a peak efficiency of 90.1%. The antenna has been integrated and simulated in a model Mini Cooper to test the effectiveness of vehicular communication. Full article

This study presents a comprehensive experimental investigation of a 2.2 kW brushless DC (BLDC) motor integrated with a three-shaft planetary gearbox, focusing on overall energy efficiency, battery state of charge (SOC) estimation, and real-time speed control under variable load conditions. In the first stage, the gearbox transmission ratio was experimentally verified to establish the kinematic relationship between the BLDC motor and the eddy current dynamometer shafts. In the second stage, the motor was operated in open loop mode at fixed reference speeds while variable load torques ranging from 1 to 7 N.m were applied using an AVL dynamometer. Electrical voltage, current, and rotational speed were measured in real time through precision transducers and a data acquisition interface, enabling computation of overall efficiency and SOC via the Coulomb counting method. The open loop results demonstrated that maximum efficiency occurred in the intermediate-to-high-speed region (2000 to 2800 rpm) and at higher load torques (5 to 7 N.m) while locking the third gearbox shaft produced negligible parasitic losses. In the third stage, a proportional–integral–derivative (PID) controller was implemented in closed loop configuration to regulate motor speed under the same variable load scenarios. The closed loop operation improved the overall efficiency by approximately 8–20 percentage points within the effective operating range of 1600–2500 rpm, reduced speed droop, and ensured precise tracking with minimal overshoot and steady-state error. The proposed methodology provides an integrated experimental framework for evaluating the dynamic performance, energy efficiency, and battery utilization of BLDC motor planetary gearbox systems, offering valuable insights for electric vehicle and hybrid electric vehicle (HEV) drive applications. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

Lithium-ion batteries (LIBs) dominate energy storage for electric vehicles (EVs) due to their high energy density, long cycle life, and low self-discharge. However, high costs, complex manufacturing, and the requirement for advanced battery management systems (BMSs) constrain their broader deployment. Therefore, extending the utility of LIBs through reuse is essential for economic and environmental sustainability. Retired EV batteries with 70–80% state-of-health (SOH) can be repurposed in battery energy storage systems (BESSs) to support power grids. Effective reuse depends on accurate and rapid assessment of SOH and state-of-safety (SOS), which relies on precise state-of-charge (SOC) detection, particularly for aged LIBs with elevated thermal and electrochemical risks. This review systematically surveys SOC, SOH, and SOS detection methods for second-life LIBs, covering model-based, data-driven, and hybrid approaches, and highlights strategies for a fast and reliable evaluation. It further examines power electronics topologies and control strategies for integrating second-life LIBs into power grids, focusing on safety, efficiency, and operational performance. Finally, it analyzes key factors within the closed-loop supply chain, particularly reverse logistics, and provides guidance on enhancing adoption and supporting the establishment of circular battery ecosystems. This review serves as a comprehensive resource for researchers, industry stakeholders, and policymakers aiming to optimize second-life utilization of traction LIBs. Full article

The increasing demand for reliable and efficient power electronic systems in critical applications—such as renewable energy, electric vehicles, and aerospace—has intensified the need to understand and predict failure mechanisms in power devices. This work focuses on the reliability assessment and lifetime modeling of medium-voltage power electronic components under realistic mission profiles. By combining accelerated aging tests, failure analysis, and physics-of-failure modeling, we identify dominant degradation mechanisms such as thermal cycling, partial discharge, and dielectric break-down. A hybrid methodology is proposed, integrating experimental data and simulation to predict the evolution of key parameters (e.g., on-state resistance, threshold voltage) over time. The study also explores the impact of packaging, thermal management, and environmental stresses on device robustness. The results provide valuable insights into the design of more durable power electronic converters and for the implementation of condition monitoring strategies in real-time applications. Full article