Search Results (39)

This study presents the design and experimental testing of a two-degrees-of-freedom (2DOF) elbow prosthesis prototype designed to replicate the movement patterns of a native or normal human elbow. Two methods of the control of the prosthesis, namely, the proportional–integral–derivative method (PID; a well-established method) and a combination of sliding mode control with a time base generator strategy (SMC + TBG; an advanced method), were compared on the basis of various performance metrics of the prosthesis, as obtained in laboratory tests. Among these metrics were the angular displacement and velocity as a function of time. The mechanical design combined 3D-printed components with custom-designed joints, featuring a worm gear transmission with a crown gear for flexion–extension, enhanced by torsional springs, and a pinion gear with a crown gear for pronation–supination and control. Sensors for voltage and current data acquisition enabled real-time monitoring and control. The prosthesis was tested in the laboratory with a range of motion of 100–120° for flexion–extension, 50° for supination, and 75° for pronation, demonstrating the adaptability of the actuators and validating their autonomy through battery-powered operation. The results showed that control using SMC + TBG resulted in biomimetic patterns for angular displacement and angular velocity of the prosthesis, whereas control using PID did not. Thus, the prosthesis with control provided using an SMC + TBG strategy may have been promised for use by people who have undergone transhumeral amputation. Full article

This article presents the development of a compact, high-precision, and energy-efficient temperature monitoring system designed for tracking applications where continuous and accurate thermal monitoring is essential. Built around the HY0020 System-on-Chip (SoC), the system integrates two bandgap-based temperature sensors—one internal to the SoC and one external (Si7020-A20)—mounted on a custom PCB and powered by a coin cell battery. A distinctive feature of the system is its support for real-time parameterization of the internal sensor, which enables advanced capabilities such as thermal profiling, cross-validation, and onboard diagnostics. The system was evaluated under both room temperature and refrigeration conditions, demonstrating high accuracy with the internal sensor showing an average error of 0.041 °C and −0.36 °C, respectively, and absolute errors below ±0.5 °C. With an average current draw of just 0.01727 mA, the system achieves an estimated autonomy of 6.6 years on a 1000 mAh battery. Data are transmitted via Bluetooth Low Energy (BLE) to a Raspberry Pi 4 gateway and forwarded to an IoT cloud platform for remote access and analysis. With a total cost of approximately EUR 20 and built entirely from commercially available components, this system offers a scalable and cost-effective solution for a wide range of temperature-sensitive applications. Its combination of precision, long-term autonomy, and advanced diagnostic capabilities make it suitable for deployment in diverse fields such as supply chain monitoring, environmental sensing, biomedical storage, and smart infrastructure—where reliable, low-maintenance thermal tracking is essential. Full article

Background and Objectives: Vision significantly contributes to postural control, balance, coordination, and body kinematics, thus deeply influencing everyday functionality. Sight-impaired subjects often show upper body anatomofunctional and kinetic chain alterations negatively impacting daily living efficiency and autonomy. The present study aimed to investigate and train, for the first time, upper body sensorimotor control in an Italian blind baseball team to boost global and segmental functionality while contemporarily prevent injuries. Materials and Methods: The whole team underwent a validated test battery using both quantitative traditional tools, such as goniometric active range of motion and muscular/functional tests, and an innovative biofeedback-based device, a Libra proprioceptive board. Consequently, a 6-week adapted training protocol was designed and leaded to improve sensorimotor control and, hence, counteract disability-related deficits and sport-specific overuse syndromes. Results: Statistically significant improvements were observed in all the investigated parameters. Noteworthy, an overall boost of global and segmental stability was detected through an orthostatic dynamic balance enhancement during the Y Balance test (p = 0.01) and trunk multiplanar control improvement on the Libra board (p = 0.01). Concurrently, the comparison of baseline vs. post-intervention outcomes revealed a consistent increase in upper body mobility (p < 0.05 for all the assessed districts), core recruitment (p = 0.01 for all the administered functional tests), and proprioceptive postural control (p = 0.01 for the Libra board validated test). Conclusions: Our findings suggest that a tailored sensorimotor training, conceived and led by an adapted physical activity kinesiologist, may effectively improve upper body functional prerequisites and global proprioceptive control, thus potentially promoting autonomy, quality of life, and physical activity/sport practice adherence in visually impaired individuals. Full article

The pursuit of sustainable and environmentally friendly transportation has led to the exploration of alternative fuel sources, among which hydrogen stands out prominently. This work delves into the potential of hydrogen fuel for internal combustion engines (ICEs), emphasizing its capacity to ensure the required performance levels while concurrently enhancing overall efficiency. The integration of a mild hybrid powertrain in a small size passenger car was considered for obtaining a twofold advantage: mitigating power loss due to low volumetric efficiency and increasing fuel economy. A comprehensive approach combining 0D/1D modeling simulations and experimental validations was employed on a gasoline-powered small size ICE, considering its conversion to hydrogen, and mild hybridization. Vehicle simulations were performed in AVL Cruise M and validated against experimental data. Various electric motors were scrutinized for a small size battery pack typical of mild hybrid vehicles. Furthermore, the paper assesses the potential range achievable with the hydrogen-powered hybrid vehicle and compares it with the range reported by the manufacturer for the original gasoline and pure electric version. In terms of global results, these modifications were found to successfully improve efficiency compared to baseline gasoline and hydrogen fueling. Additionally, performance gains were achieved, surpassing the capabilities of the original gasoline vehicle despite its intrinsic volumetric efficiency limitations when using hydrogen. Along with the conversion to hydrogen and thus zero-carbon tail-pipe emissions, incorporating a Start/Stop system, and the integration of mild hybrid technology with energy recuperation during braking, overall efficiency was enhanced by up to 30% during urban use. Furthermore, the hybridization implemented in the H₂ version allows an autonomy comparable to that of the electric vehicle but with evident shorter refilling times. Specific aspects of the 48 V battery management are also scrutinized. Full article

This paper delves into the application of vibration-based energy harvesting to power environmental sensor nodes, a critical component of modern data collection systems. These sensor nodes play a crucial role in structural health monitoring, providing essential data on external conditions that can affect the health and performance of structures. We investigate the feasibility and efficiency of utilizing piezoelectric vibration energy harvesters to sustainably power environmental wireless sensor nodes on the one hand. On the other hand, we exploit different approaches to minimize the sensor node’s power consumption and maximize its efficiency. The investigations consider various sensor node platforms and assess their performance under different voltage levels and broadcast frequencies. The findings reveal that optimized harvester designs enable real-time data broadcasting with short intervals, ranging from 1 to 3 s, expanding the horizons of environmental monitoring, and show that in case the system includes a battery as a backup plan, the battery’s lifetime can be extended up to 9 times. This work underscores the potential of vibration energy harvesting as a viable solution for powering sensor nodes, enhancing their autonomy, and reducing maintenance costs in remote and challenging environments. It opens doors to broader applications of sustainable energy sources in environmental monitoring and data collection systems. Full article

This article presents a comprehensive data-driven approach on enhancing grid-connected microgrid grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine-learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, training a Long Short-Term Memory (LSTM) network for forecasting solar irradiance and load profiles with a dataset spanning from 2009 to 2018, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of battery state of charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The proposed methodology and optimization process demonstrate their versatility and applicability to a wide range of microgrid design scenarios comprising solar PV and battery energy storage systems (BESS), making them a valuable resource for enhancing grid resilience and economic efficiency across diverse settings. The results highlight the potential of the proposed approach in strengthening grid resilience by improving autonomy, reducing downtime by 25%, and fostering sustainable energy utilization by 82%. Full article

It is widely expected that electric vehicles will be able to satisfy most road transport needs. The aim of this paper is to provide an answer to how far the electrification of buses used for local public transport can be pursuable through insight into the engineering problems and range, i.e., the autonomy on battery, as well as the efficiency of recharging systems. At first, a wide survey of the main solutions that are present on the market, or foreseen for the near future, concerning the electrification of fleets for LPT is provided. Thereafter, such solutions are compared through numerical applications and by using a practical case study, pertaining to the city of Turin (IT), where static inductive charging has been extensively experienced. Particular attention is paid to engineering problems and to the autonomy on battery of the vehicles as a function of their mass, electric energy storage system, charging opportunities and infrastructure, while comparing the time and efficiency of recharging systems. The authors conclude by recommending the most promising alternatives for battery electric buses while outlining their limits, striving to provide for the literature a research instrument, which is lacking, for delimitating the applicability of electric buses for LPT while outlining the viable solutions. Full article

Despite abundant solar resources, Mali has remained one of the least electrified countries in the world. Besides daily life activities and the economy, the shortage of electricity has severely affected the quality of healthcare services in the country. In the absence of electrical grids, standalone photovoltaic (PV) systems could be an alternative option in Mali for the electrification of isolated community health centers. However, because standalone PV systems are highly weather-dependent, they must be properly sized according to the local weather conditions. This paper presents the optimal sizing of standalone PV systems for the electrification of community health centers in Mali. The optimization for PV systems was performed for five different locations through simulation and modeling using PVsyst, considering the autonomy of 1 to 3 days and the probability of loss of load for 1 to 5%. Furthermore, for the economic analysis, the levelized cost of electricity (LCOE), payback period and return on investment for the standalone PV systems were calculated. Through the optimization, it was found that the standalone PV systems with PV array sizes ranging from 1650 to 2400 watts, along with 606 Ah battery storage, would be suitable to supply the daily energy demand for community health centers anywhere in the country. Moreover, by only replacing the 606 Ah battery storage with 1212 Ah and 1818 Ah sizes, the PV systems would be able to help and keep the energy reserves for 2 and 3 autonomous days, respectively. Furthermore, the results show that in comparison to a LCOE of 0.94–0.98 USD/kWh for a diesel generator, the LCOE for the standalone PV system would range from 0.23 to 0.46 USD/kWh without discounted rates and from 0.33 to 0.60 USD/kWh if discounted at 6%. In addition to a lower LCOE, the saving of 46–76 tons of CO₂ during the project’s lifespan, the short payback periods and high return of investment (ROI) values make standalone PV systems a suitable electrification option for Mali. Considering the total expenses, LCOE, payback period, and ROI, standalone PV systems for community health centers were found to be economically viable in all cases for Mali. Full article

This research focuses on the measurement of the solar generation potential on the roads of the Andean city of Cuenca, Ecuador, and its application in electric vehicles. The tests were conducted in real environments, whereby natural and artificial structures obstruct direct radiation to the panel during the trajectory. An initial study is presented with daily operating conditions, using an urban bus route as a case study. The methodology used consists of taking measurements on different days and weather conditions to evaluate the photovoltaic generation and its contribution to the energy autonomy of the electric vehicle. Additionally, the energy autonomy between the electric vehicle with its factory configuration versus the one equipped with the solar panel is compared. For this purpose, a photovoltaic panel is installed on the roof of the vehicle, connected to a control system that monitors the radiation and current data, regulating the charging and discharging of the batteries. The aim is to demonstrate that the installation of solar panels on electric vehicles can significantly increase their energy autonomy. The contribution of this research could serve as an initial guide for governments and private companies to make decisions on the deployment of electric buses, electric vehicles and other vehicles integrated with solar photovoltaic energy, taking into account their routes. The findings of the study reveal that the implementation of the mobile charging system improves the range of the electric vehicle used in this study. In detail, an average increase of 40% in range was achieved in favorable environmental conditions and an increase of 14% in unfavorable environmental conditions. It is important to highlight that Cuenca has favorable conditions for solar systems due to its geographical location: altitude, hours of radiation and angle of incidence. Full article

Mobile robots can perform tasks on the move, including exploring terrain, discovering landmark features, or moving a load from one place to another. This group of robots is characterized by a certain level of intelligence, allowing the making of decisions and responding to stimuli received from the environment. As part of Industry 5.0, such mobile robots and humans are expected to co-exist and work together in a shared environment to make human work less tiring, quicker, and safer. This can only be realized when clean, dense, and economical energy sources are available. The aim of the study is to analyze the state of the art and to identify the most important directions for future developments in energy sources of robotic power systems based mainly on batteries. The efficiency and performance of the battery depends on the design using different materials. Work environments and performance requirements are considered in this systematic review to classify solutions that help developers choose the best-suited power system for specific application. Indirectly, the aim of the work is to generate discussion within the scientific and engineering community. A narrative review of publications from six major bibliographic databases according to preset inclusion criteria is combined with a critical analysis of current and future technologies. The main findings of the review allow answering the question of what is the role of modern power source technologies, artificial intelligence, and ground-breaking research work in global policies related to energy saving, green policies, and sustainable development. The main opportunities and threats are discussed, and a brief feasibility analysis is carried out. The novelty of the article relates not only to the analysis of technologies, but also to approaches and their use under conditions of limited resource availability, when resource usage must be minimized. The article provides an overview of batteries, their specifications, classifications, and their advantages and disadvantages. In addition, we propose (1) an algorithm for selecting the main energy source for robot application, and (2) an algorithm for selecting an electrical system power supply. Current mobile robot batteries are, in most cases, the robot’s biggest limitation. Progress in battery development is currently too slow to catch up with the demand for robot autonomy and range requirements, limiting the development of mobile robots. Further intensive research and implementation work is needed to avoid years of delay in this area. Full article

The development of hybrid electric vehicles (HEVs) is rapidly gaining traction as a viable solution for reducing carbon emissions and improving fuel efficiency. One type of HEV that is gaining significant interest is the fuel cell/battery/supercapacitor HEV (FC/Bat/SC HEV), which combines fuel cell, battery, supercapacitor, AC, and DC generators. These FC/B/SC HEVs are particularly appealing because they excel at efficiently managing energy and cater to a wide range of driving requirements. This study presents a novel approach for exploiting the kinetic energy of a sensorless HEV. The vehicle has a primary fuel cell resource, a supercapacitor, and lithium-ion battery energy storage banks, where each source is connected to a special converter. The obtained hybrid system allows the vehicle to enhance autonomy, support the fuel cell during low production moments, and improve transient and steady-state load requirements. The exploitation of kinetic energy is performed by the DC and AC generators that are linked to the electric vehicle front wheels to transfer the HEV’s wheel rotation into power, contributing to the overall power balance of the vehicle. The energy management system for electric vehicles determines the FC setpoint power through the classical state machine method. At the same time, a robust speed controller-based artificial intelligence algorithm reduces power losses and enhances the supply efficiency for the vehicle. Furthermore, we evaluate the performance of a robust controller with a speed estimator, specifically using the adaptive neuro-fuzzy inference system (ANFIS) and the model reference adaptive system (MRAS) estimator in conjunction with the direct torque control-support vector machine (DTC-SVM), to enhance the torque and speed performance of HEVs. The results demonstrate the feasibility and reliability of the vehicle while utilizing the additional DC and AC generators to extract free kinetic energy, both of which contributed to 28% and 24% of the total power for the vehicle, respectively. This approach leads to a vehicle supply efficiency exceeding 96%, reducing the burden on fuel cells and batteries and resulting in a significant reduction in fuel consumption, which is estimated to range from 25% to 35%. Full article

Bicycles are rapidly gaining popularity as a sustainable mode of transportation around the world. Furthermore, the smart bicycle paradigm enables increased use through the Internet of Things applications (e.g., GPS tracking systems). This new paradigm introduces energy autonomy as a new challenge. The energy harvesting technology can capture the energy present in the cycling environment (e.g., kinetic or solar) to give this autonomy. The kinetic energy source is more stable and dense in this environment. There are several wheel kinetic harvesters on the market, ranging from low-complexity dynamos used to power bicycle lights to smart harvester systems that harvest kinetic energy while braking and cycling and store it for when it is needed to power sensors and other electronics loads. Perhaps the hub and the “bottle” dynamos are the most commercially successful systems because of their cost-effective design. Furthermore, the bottle generator is very inexpensive, yet it suffers from significant energy losses and is unreliable in wet weather due to mechanical friction and wheel slippage in the wheel/generator contact. This paper proposes a cost-effective bicycle harvester based on a novel kinetic-electromagnetic transducer. The proposed harvester allows for the generation and storage of harnessed kinetic energy to power low-power electronics loads when the user requires it (e.g., cell phone charging, lighting). The proposed harvester is made up of a power processing unit, a battery, and an optimized transducer based on a Halbach magnet array. An extensive full-wave electromagnetic simulation was used to evaluate the proposed transducer. Circuit simulation was also used to validate the proposed power unit. The proposed harvester generates a simulated output power of 1.17 W with a power processing unit efficiency of 45.6% under a constant bicycle velocity of 30 km/h. Full article

The goal of this paper is the evaluation of lithium-ion batteries that power electric vehicles (EVs) under variable climatic conditions to determine how the driving range of a vehicle is modified because of changes in battery performance caused by the variability of environmental conditions. The influence of sudden changes in ambient temperature on the performance of the battery that powers electric vehicles has been studied and analyzed. The study is focused on how trips across geographical zones with different climates affect the autonomy of an electric vehicle’s battery, and thus the driving range of the electric vehicle (EV). A model has been developed to reproduce on a laboratory scale the real conditions to which EVs are subject when circulating under fluctuating temperatures, which force the battery to operate in a transient or non-steady state. A simulation has been run for different climatic conditions to evaluate the performance of the battery and the driving range of the electric vehicle under variable operating conditions. A laboratory prototype has been designed and built to validate the modeling and to adjust the theoretical approach to experimental values through the corresponding correction factor in case significant deviations occur. The model has been validated for a simulated route that reproduces a real driving trip for specific geographical areas. The model indicates that there is a shortening in the global driving range of 43.5 km over a trip distance of 538 km, which means there is a reduction of 8%. The simulation has been applied to a specific geographical area in the nearby of the city of Lyon (France), for a temperature gap of 39 °C, from −6 °C to 33 °C, but can also be used for almost any other zones, although the reduction in driving range may vary because of specific climate conditions. Full article

Monitoring the tortoise Chelonoidis chilensis in the wild, currently in a vulnerable state of conservation in southern Argentina, is essential to gather movement information to elaborate guidelines for the species preservation. We present here the electronic circuit design as well as the associated firmware for animal monitoring that was entirely designed by our interdisciplinary research team to allow the extension of device features in the future. Our development stands out for being a family of low-cost and low-power devices, that could be easily adaptable to other species and contexts. Each device is composed of a sub 1 GHz radiofrequency IoT-compatible transceiver, a global navigation satellite system (GNSS) receiver, a magnetometer, and temperature and inertial sensors. The device does not exceed 5% of the animal’s weight to avoid disturbance in their behavior. The board was designed to work as a monitoring device as well as a collecting data station and a tracker, by adding only small pieces of hardware. We performed field measurements to assess the autonomy and range of the radiofrequency link, as well as the power consumption and the associated positioning error. We report those values and discuss the device’s limitations and advantages. The weight of the PCB including battery and GNSS receiver is 44.9 g, its dimensions are 48.7 mm × 63.7 mm, and it has an autonomy that can vary between a week and a month, depending on the sampling rates of the sensors and the rate of the RF signal and that of the GNSS receiver. The characterization of the device parameters will favor the open use of this development by other research groups working on similar projects. Full article

Powertrain electrification in the agricultural vehicles is still in the initial stages. This article analyzes the energy behavior of a Photovoltaic/Fuel Cell Agricultural Mobile Robot (PV/FCAMR) as the preliminary step before development. This concept incorporates three energy storage sources for the powertrain: a battery pack, a Fuel Cell (FC) system, and a Photovoltaic (PV) system. This paper proposes an approach based on the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms to determine the sizes of the FC and battery of an FCAMR. A differential drive mobile robot was used as a case study to extract the typical working cycles of farming applications. The FCAMR vehicle model was developed in MATLAB/Simulink to evaluate vehicle energy consumption and performance. For the energy analysis and evaluation, the FCAMR was tested based on two realistic working cycles comprising circular and rectangular moving patterns. The results showed that the proposed arrangement could extend the FCAMR autonomy by 350% as opposed to the pure electric system. This allows for at least 8 h of work with a tank filled with 150 g hydrogen and a PV system with a 0.5 m${}^2$ monocrystalline solar panel. The simulation results have demonstrated the relevance and robustness of this approach in relation to various working cycles. The cost comparison between the theoretical and optimization sizing methods showed at least an 8% decrease for the FCAMR. Furthermore, adding the PV system extended the vehicle’s range by up to 5%. This study provides an optimal solution for energy sources sizing of mobile robots as futuristic agricultural vehicles. Full article