Search Results (597)

Energy-aware Adaptive Cruise Control (Eco-ACC) has become an essential approach for enhancing the energy efficiency of electric vehicles while ensuring safe and comfortable driving. This paper presents a systematic review, following the PRISMA methodology, of 60 recent studies published between 2021 and 2025. The review provides a structured analysis of control strategies, validation approaches, computational demands, and battery-related considerations in Eco-ACC systems. The results indicate that Model Predictive Control (MPC) remains the most widely adopted technique (41.7%), primarily due to its ability to handle system constraints and address multi-objective optimization problems. Reinforcement Learning (RL) approaches (33.3%) are increasingly explored for their capability to adapt to uncertain and dynamic driving conditions. In addition, hybrid MPC–AI methods (16.7%) show strong potential for balancing optimal control performance with real-time implementation requirements. A key observation is the clear imbalance in validation practices: more than 73% of the studies rely on simulation-based evaluation, whereas only 10% include real-world experiments, revealing a pronounced simulation-to-reality (sim2real) gap. Furthermore, two critical research gaps are identified. First, the computational energy paradox highlights the trade-off between improved control performance and increased computational cost. Second, battery-aware control remains insufficiently addressed, as most existing methods overlook long-term battery degradation effects. Based on these findings, this review proposes a deployment-oriented research framework that prioritizes hybrid control architectures, real-time feasibility, and robust validation strategies, including Hardware-in-the-Loop and field testing. The presented insights aim to support the development of practical and energy-efficient Eco-ACC systems suitable for real-world deployment in next-generation electric vehicles. Full article

This study analyzes how dynamic disturbances influence the decisions made during the human supervision of autonomous unmanned aerial vehicles. While previous research has primarily focused on control algorithms and system stability, the effect of disturbances originating from system dynamics on operator intervention behavior has been less extensively investigated. To examine this problem, a hardware-in-the-loop (HIL) experimental framework was developed, which is based on a previously validated unmanned aerial vehicles (UAVs) test platform and was adapted in this study to enable the investigation of human supervisory decision-making. Participants observed the behavior of an autonomously operating system under controlled disturbances and were provided with the possibility to intervene by activating an emergency landing mechanism. The results indicate that the disturbance intensity had a significant effect on intervention decisions, while the reaction times did not show notable differences. This finding suggests that supervisory behavior is primarily determined by the evaluation of the system state rather than by timing characteristics. It also identifies that subjective risk perception plays a decisive role in the formation of intervention decisions, indicating the presence of an implicit decision threshold for participant behavior. The research findings offer a novel approach to the interpretation of human–UAV interaction by emphasizing the role of system dynamics in shaping user decisions. The presented method may provide a foundation for the development of predictive and adaptive supervisory systems that take into account the characteristics of human decision-making, thereby contributing to the design of safer and more efficient autonomous systems. Full article

Hardware-in-the-Loop (HiL) driving simulators are increasingly adopted in vehicle development to improve efficiency, flexibility, and repeatability across the product life cycle. Their implementation, however, remains challenging, as the integration of real vehicle components into simulation environments significantly increases system complexity and requires the coherent interaction of real hardware, actuation subsystems, and numerical models representing non-physical components. This paper addresses these challenges through the development of a remotely controlled HiL test rig for the braking system, focusing on its integration with a driver’s station in a driving simulator. The role of braking systems within HiL simulators is first discussed, highlighting their relevance for early development, debugging, and calibration activities. An exemplary development pipeline is then presented, introducing a modular and scalable software architecture implemented in MATLAB/Simulink to manage remote brake actuation and force feedback. The performance of the proposed actuation system is experimentally evaluated and discussed, including its integration with a commercial force-feedback device. The results demonstrate the feasibility and effectiveness of the proposed framework, showing concrete benefits in respect of development efficiency and industrial applicability. Full article

To improve the state-of-charge (SOC) estimation accuracy of sodium-ion batteries under complex operating conditions, this paper proposes a particle swarm optimization-based heterogeneous adaptive extended Kalman filter. A hardware-in-the-loop (HIL) validation platform is also established to reproduce the sampling-chain constraints of a practical battery management system. In addition, a second-order equivalent circuit model (ECM) serves to characterize battery dynamics and generate validation data. Within this framework, the degradation in estimation performance from the theoretical environment to practical hardware execution is quantitatively analyzed. The feasibility of using ECM-generated data for SOC estimation algorithm validation is also evaluated. Using measured Federal Urban Driving Schedule data at 25 °C, the proposed method achieves high estimation accuracy and stable convergence in both environments. Specifically, the mean absolute error and root-mean-square error in the theoretical environment are 0.11% and 0.25%, respectively. Under HIL conditions, the corresponding values are 0.60% and 0.63%. Additional tests under different temperatures and composite disturbance conditions further verify the adaptability and robustness of the proposed algorithm. The results also show that practical hardware constraints introduce non-negligible performance degradation. In addition, ECM-generated data remain highly consistent with measured data in terms of error-evolution trends. Therefore, ECM-generated data can serve as a feasible validation data source for SOC estimation algorithm performance evaluation and rapid validation. Full article

Optimizing the trade-off between temperature-tracking precision and energy efficiency remains a significant challenge in industrial refrigeration systems. To address this, this paper presents a novel hierarchical control architecture combining Model Predictive Control (MPC), Fuzzy Logic Controller (FLC), and an Extended State Observer (ESO). Specifically, the MPC manages the system’s physical constraints, while the FLC dynamically tunes the objective function weights online, ensuring an optimal balance between performance and energy savings. Furthermore, the ESO is employed to estimate and actively compensate for exogenous heat load disturbances and model uncertainties. Comparative results confirm that the proposed strategy not only reduces energy consumption by 10.93% but also achieves highly disturbance rejection when compared to conventional PI control. The practical feasibility of the proposed algorithm is rigorously validated via hardware-in-the-loop (HIL) simulations utilizing an STM32F767ZI microcontroller. The successful hardware-in-the-loop validation on an embedded microcontroller demonstrates the industrial viability of the proposed architecture, proving it to be a highly deployable and cost-effective solution for refrigeration system. Full article

Underwater wireless optical communication (UWOC) for autonomous underwater vehicles is severely compromised by the coupling of oceanic optical turbulence and platform motion. Traditional static statistical models fail to capture the temporal evolution of these stochastic processes, hindering effective real-time beam tracking. This paper proposes a unified dynamic framework and a hybrid intelligent control strategy to address beam misalignment in turbulent environments. First, a physically motivated stochastic differential equation (SDE) model is derived from the Radiative Transfer Equation via diffusion approximation. Validated by an inverse Fokker–Planck approach, this model accurately reconstructs drift fields for diverse channel conditions, serving as a dynamic generator for time-varying fading. Second, to maintain robust link alignment, a hybrid Fuzzy-Reinforcement Learning control strategy is developed. This approach integrates the interpretability of fuzzy logic with the adaptive optimization of Q-learning, incorporating a supervisor mechanism to handle deep fading events. Numerical simulations and hardware-in-the-loop (HIL) experiments demonstrate the system’s efficacy. The proposed controller achieves a median alignment error of 3.64 mm and reduces transient errors by over 80% compared to classical PID controllers during signal recovery. These results confirm that the proposed framework significantly enhances link stability and tracking robustness for AUVs in complex random media. Full article

With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article

Ship propulsion electrification is an important enabler towards a sustainable shipping industry. Ship power systems are turning into modern microgrids integrating different generation/storage resources, converter technologies and electric propulsion, utilizing different control levels and communication systems. The definition of comprehensive test requirements, set-ups and procedures is critical to ensure that the equipment will behave as expected in the ship system context. Comprehensive testing is becoming increasingly challenging due to complex interactions at the system level, attributed to electrical, mechanical/hydrodynamic, control, protection, and information and communication systems present in modern and future ships. Standardization has addressed the testing of several individual components, as well as specific system tests for marine applications; however, a holistic testing approach is missing. This paper reviews the generic and maritime standards for testing ship electric power propulsion systems and equipment, focusing on generators/motors, power electronic drives and onshore power supply systems. A review of the scientific literature is performed, classifying the publications according to the testing method, such as pure hardware tests, co-simulation and hardware in the loop simulation (HIL). The need for holistic testing of shipboard microgrids is explained. A holistic HIL testing approach is proposed, which integrates hardware controllers and power equipment of different manufacturers and functions, in order to reduce the complexity and cost of sea trials. The proposed approach is accompanied by example implementation and application guidelines. Full article

This paper presents the design, implementation, and experimental validation of a real-time embedded photovoltaic (PV) emulator based on the two-diode model, using a dSPACE DS1103 platform for hardware validation. The proposed system aims to accurately reproduce the electrical behavior of PV modules under varying environmental conditions, including irradiance and temperature variations. The emulator architecture combines a lookup-table-based modelling approach with a programmable DC power source, enabling deterministic real-time execution and efficient implementation. A multi-level control structure is employed, integrating inner-loop regulation, model-based reference generation, and feedback control to ensure accurate tracking of the PV current–voltage (I–V) characteristics. Experimental results demonstrate that the emulator achieves high accuracy, with an approximation error of approximately 1.2% under standard operating conditions. The system exhibits stable dynamic behavior characterized by a time constant of approximately 0.5 s, with performance maintained across different sampling intervals and load conditions. Additional simulations confirm that the two-diode model preserves high accuracy over a temperature range of 15–60 °C, with deviations below 2%. The results highlight that the two-diode model provides an optimal trade-off between modelling accuracy and computational complexity for real-time embedded applications. The proposed emulator offers a flexible and reliable platform for laboratory validation of photovoltaic behavior and provides the foundation for future testing of maximum power point tracking (MPPT) algorithms, power electronic converters, and embedded control strategies under controlled conditions. Full article

Extreme fast charging (XFC) infrastructure is becoming increasingly necessary as the number of electric vehicles continues to grow. However, deploying such stations introduces several challenges related to power quality and compliance with regulatory standards. This work presents an alternative XFC station designed for charging multiple vehicles while ensuring low harmonic distortion in the grid currents, without the need for sinusoidal filters, by employing the Zero Harmonic Distortion (ZHD) converter. The proposed system offers galvanic isolation for each charging interface and supports additional functionalities, including the integration of Distributed Energy Resources (DERs) and the provision of ancillary services. These features are enabled through the combination of a bidirectional grid-connected active front-end operating at low switching frequency with high-frequency silicon carbide (SiC)-based dc/dc converters on the vehicle side. Hardware-in-the-loop (HIL) simulation results demonstrate a total demand distortion (TDD) of 1.12% for charging scenarios involving both 400 V and 800 V battery systems, remaining within the limits specified by IEEE 519-2022. Full article

Accurately monitoring the wheelset–rail friction condition is crucial for ensuring the safety and operational efficiency of the traction drive system. However, the friction characteristics of wheelsets are easily influenced by factors such as ramp transitions and variable railway conditions in the complex environment. These factors significantly increase the difficulty of detecting friction anomalies and accurately locating faulty wheelsets in a timely manner. To address this issue, this paper proposes a sliding mode coordinate positioning–based friction anomaly monitoring scheme for multiple wheelsets in traction drive systems. First, a multi-sliding mode fusion-based friction characteristic observer is developed. Then, an friction coordinate analysis-based anomaly identification method is proposed. Finally, the proposed method is validated on a hardware-in-the-loop (HIL)-based experimental platform. Experimental results demonstrate that the proposed scheme can effectively detect friction anomalies and accurately locate abnormal wheelsets in multi-wheelset traction systems. Compared with traditional methods, the proposed scheme exhibits stronger robustness to varying railway conditions and does not require complex optimization mechanisms, making it suitable for practical on-board applications. Full article

To improve the anti-slip performance and energy-efficient torque coordination of four-wheel-drive articulated tractors operating in hilly and mountainous terrains, this study proposes an integrated control framework that combines a 7-DOF tractor dynamics model, a GA-optimized fuzzy slip-ratio controller, and a three-level dynamic torque allocation strategy. First, a control-oriented full-vehicle dynamics model is established by integrating tractor body dynamics, wheel rotational dynamics, and the Dugoff tire model. Then, a fuzzy slip-ratio controller is designed using the slip-ratio tracking error and its rate of change as inputs, and its key parameters are optimized using a genetic algorithm. On this basis, a three-level dynamic torque allocation strategy is developed to coordinate the four in-wheel motors according to wheel-load distribution and slip-related constraints. MATLAB/Simulink (version 2023a) simulations and hardware-in-the-loop (HIL) tests are carried out to validate the proposed strategy. Under the straight-line driving condition, the RMSE of the proposed GA-fuzzy controller is reduced from 0.02716 to 0.00897. Under the steering condition, the average RMSE is reduced from 0.02079 to 0.01003. In addition, under the torque-allocation validation condition, the average four-wheel RMSE is reduced from 0.29632 under equal torque allocation to 0.02159 under the proposed three-level dynamic torque allocation strategy. The results indicate that the proposed method can effectively maintain the slip ratio near its target value, suppress excessive slip and redundant torque output, and improve the anti-slip and energy-efficient performance of articulated tractors. More importantly, the study provides an integrated control framework that unifies GA-optimized slip regulation and three-level torque coordination specifically for four-wheel-drive articulated tractors. Full article

We developed a hardware-in-the-loop simulation of a battery management system (BMS) using controller area network (CAN) as the communication backbone for electric-powered response boats in flood rescue. A LiFePO4 pack and discharge motor/charger were modeled in MATLAB/Simulink/Simscape, while an STM32 Nucleo-F446RE executed CAN messaging. The BMS monitored voltage, current, temperature, and state of charge. Results indicate CAN’s reliability under rescue-like disturbances: priority arbitration delivered over-temperature and over-current warnings ahead of routine telemetry; error detection and retransmission preserved data integrity; and bus-load analysis showed low latency for urgent frames without interrupting state-of-charge reporting, improving situational awareness and reducing operator risk. Full article

The rapid electrification of transportation and proliferation of distributed energy resources (DERs) are transforming distribution grids into highly dynamic, data-intensive, and cyber-physical systems. While reinforcement learning (RL), multi-agent coordination, and edge computing offer powerful tools for adaptive control, their deployment in safety-critical utility environments raises concerns regarding stability, certification compatibility, cyber-resilience, and regulatory acceptance. This paper presents an architecture-centric framework for edge-intelligent and cyber-resilient coordination of electric vehicles (EVs) and DERs that reconciles adaptive learning with deterministic safety guarantees. The proposed hierarchical edge–cloud architecture integrates multi-agent system (MAS) coordination, constraint-invariant reinforcement learning, and embedded cybersecurity mechanisms within a structured control hierarchy. Learning-enabled edge agents operate exclusively within standards-compliant safety envelopes enforced through supervisory constraint projection, control barrier functions, and Lyapunov-consistent stability safeguards. Protection-critical functions remain deterministic and isolated from adaptive layers, preserving compatibility with IEEE 1547 and existing utility protection schemes. The framework further incorporates anomaly triggered policy freezing, fail-safe fallback modes, and communication-aware resilience mechanisms to prevent unsafe transient behavior in non-stationary, distributed environments. Unlike simulation-only learning approaches, the architecture embeds progressive validation through software-in-the-loop (SIL), hardware-in-the-loop (HIL), and power hardware-in-the-loop (PHIL) testing to empirically verify transient stability, constraint compliance, and cyber-resilience under realistic timing and disturbance conditions. Beyond technical performance, the paper situates edge intelligence within standards evolution, governance structures, workforce transformation, techno-economic assessment, and equitable deployment pathways. By framing adaptive control as a bounded, auditable augmentation layer rather than a disruptive replacement for certified infrastructure, the proposed architecture provides a pragmatic roadmap for evolutionary modernization of distribution systems. Full article

This paper presents a deterministic embedded control architecture for an industrial electroplating line. The validated system includes two autonomous trolleys, 18 station-aligned process positions, shared-track motion, and redundant grouped baths. The proposed controller addresses the limitations of rigid sequential automation by combining asynchronous finite-state trolley execution, runtime allocation of equivalent technological stations, dwell-time-preserving retrieval, distributed thermal supervision, and layered fail-safe protection within a single ATmega2560-based implementation. The core contribution is the integration of virtual process groups and temporal FIFO logic into a compact plant-side embedded controller. This enables adaptive bath selection and process-completion-based retrieval without reliance on a real-time operating system or a computationally heavy supervisory runtime. The architecture also incorporates predictive pre-start validation, runtime software arbitration, hardware-wired interlocks, binary-coded trolley positioning, and a distributed 1-Wire thermal measurement network. Validation was performed in a controller-centered hardware-in-the-loop representation of an 18-station zinc electroplating line. Over a 100-batch horizon, the proposed architecture reduced makespan from 1642 min to 1244 min, corresponding to a 24.2% throughput improvement. Average trolley idle time decreased from 18.4 min/batch to 4.1 min/batch. Grouped-bath utilization increased from 64% to 91%, while tracked bottleneck incidents decreased from 18 to 2. These results show that adaptive, resource-aware, and safety-layered electroplating control can be realized effectively on a compact embedded platform in an industry-representative HIL setting, while preserving dwell-time integrity and controller-level safety invariants. Full article