Search Results (77)

This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter $\alpha$. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications. Full article

Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational data, how energy consumption and charging behavior affect battery aging and how operational strategies can be optimized to extend battery life under realistic conditions. This article presents an energy consumption analysis with respect to ambient temperatures and average vehicle speed based exclusively on real-world data of an urban bus fleet, providing a data foundation for range forecasting and infrastructure planning optimized for public transport needs. Additionally, the State of Charge (SOC) window during operation and vehicle idle time as well as the charging power were analyzed in this case study to formulate recommendations towards a more battery-friendly treatment. The central research question is whether battery-friendly operational strategies—such as reduced charging power and lower SOC windows—can realistically be implemented in daily public transport operations. The impact of the recommendations on battery lifetime is estimated using a battery aging model on drive cycles. Finally, the reduction in CO₂ emissions compared to diesel buses is estimated. Full article

In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R²) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance. Full article

Increasing developments in the offshore energy sector have led to demand for robotics use in inspection, maintenance, and repair maintenance tasks, particularly for the service life extension of structures. These robots experience slippage due to varying surface conditions caused by environmental factors and marine growth, leading to inconsistent traction forces and potential mission failures in single-drive systems. This paper explores control strategies and mechanical configurations both in simulation and on the physical industrial robot to mitigate slippage in offshore robotic operations, improving reliability and reducing costs. This study examines mechanical and control modifications such as multi-wheel drive (MWD), PID velocity control, and a feedback-linearized slip control system with an individual wheel disturbance observer to detect surface variations. The results indicate that a 3 WD setup with slip control handles the widest range of conditions but suffers from high control effort due to chattering effects. The simulations show potential for slip control; practically, challenges arise from low sampling rates compared to traction changes. In real-world conditions, a PID-controlled MWD system, combined with increased normal force, achieves better traction and stability. The findings highlight the need for further investigation into the mechanical design and sensor feedback, with the refinement of slip control strategies and observer design for the offshore environment. Full article

The application of smart packaging technology in fruit and vegetable preservation has shown significant potential with the ongoing advancement of science and technology. Smart packaging leverages advanced sensors, smart materials, and Internet of Things (IoT) technologies to monitor and regulate the storage environment of fruits and vegetables in real time. This approach effectively extends shelf life, enhances food safety, and reduces food waste. The principle behind smart packaging involves real-time monitoring of environmental factors, such as temperature, humidity, and gas concentrations, with precise adjustments based on data analysis to ensure optimal storage conditions for fruits and vegetables. Smart packaging technologies encompass various functions, including antibacterial action, humidity regulation, and gas control. These functions enable the packaging to automatically adjust its internal environment according to the specific requirements of different fruits and vegetables, thereby slowing the growth of bacteria and mold, prolonging freshness, and retaining nutritional content. Despite its advantages, the widespread adoption of smart packaging technology faces several challenges, including high costs, limited material diversity and reliability, lack of standardization, and consumer acceptance. However, as technology matures, costs decrease, and degradable smart packaging materials are developed, smart packaging is expected to play a more prominent role in fruit and vegetable preservation. Future developments are likely to focus on material innovation, deeper integration of IoT and big data, and the promotion of environmentally sustainable packaging solutions, all of which will drive the fruit and vegetable preservation industry toward greater efficiency, intelligence, and sustainability. Full article

Business Finland 6G Visible project’s objective is the development of 6G-era service and architecture solutions utilizing autonomous and semi-autonomous driving, with both physical and logical computational elements and with use cases for real-life verification and validation. Finnish Meteorological Institute is focusing especially on weather- and safety-related services for autonomous vehicles. We are tailoring our road weather services for the special needs of autonomous driving, keeping in mind that autonomous vehicles are more sensitive to the harsh winter weather conditions and benefit from more accurate weather information considering the sensor systems of each vehicle. Employing weather radar-based nowcasting of more accurate short-term precipitation forecasting benefits autonomous traffic, especially in cases of heavy local precipitation by re-routing/route planning and avoiding heaviest precipitation. Evaluation of autonomous vehicles’ sensor systems’ sensitivity to harsh weather conditions allows for weather forecasting based on the real vulnerability of each vehicle. Full article

The state of charge (SOC) is a characteristic parameter that indicates the remaining capacity of electric vehicle batteries. It plays a significant role in determining driving range, ensuring operational safety, and extending the service life of battery energy storage systems. Accurate SOC estimation can ensure the safety and reliability of vehicles. To tackle the challenge of precise SOC estimation in complex environments, this study introduces an improved forgetting factor recursive least squares (IFFRLS) method, which integrates the Golden Jackal optimization (GJO) algorithm with the traditional FFRLS method. This integration is grounded in the formulation of a lithium battery state equation derived from a second-order RC equivalent circuit model. Additionally, the research utilizes the interactive multiple model unscented Kalman filter (IMMUKF) algorithm for SOC estimation, with experimental validation conducted under various conditions, including hybrid pulse power characterization (HPPC), urban dynamometer driving schedule (UDDS), and real underwater scenarios. The experimental results demonstrate that the SOC estimation method of lithium batteries based on IFFRLS-IMMUKF exhibits high accuracy and a favorable temperature applicability range. Full article

Object detection and classification in autonomous vehicles are crucial for ensuring safe and efficient navigation through complex environments. This paper addresses the need for robust detection and classification algorithms tailored specifically for Indian roads, which present unique challenges such as diverse traffic patterns, erratic driving behaviors, and varied weather conditions. Despite significant progress in object detection and classification for autonomous vehicles, existing methods often struggle to generalize effectively to the conditions encountered on Indian roads. This paper proposes a novel approach utilizing the YOLOv8 deep learning model, designed to be lightweight, scalable, and efficient for real-time implementation using onboard cameras. Experimental evaluations were conducted using real-life scenarios encompassing diverse weather and traffic conditions. Videos captured in various environments were utilized to assess the model’s performance, with particular emphasis on its accuracy and precision across 35 distinct object classes. The experiments demonstrate a precision of 0.65 for the detection of multiple classes, indicating the model’s efficacy in handling a wide range of objects. Moreover, real-time testing revealed an average accuracy exceeding 70% across all scenarios, with a peak accuracy of 95% achieved in optimal conditions. The parameters considered in the evaluation process encompassed not only traditional metrics but also factors pertinent to Indian road conditions, such as low lighting, occlusions, and unpredictable traffic patterns. The proposed method exhibits superiority over existing approaches by offering a balanced trade-off between model complexity and performance. By leveraging the YOLOv8 architecture, this solution achieved high accuracy while minimizing computational resources, making it well suited for deployment in autonomous vehicles operating on Indian roads. Full article

In this study, the Bald Eagle Search Algorithm performed hydrogen consumption and battery cycle optimization of a fuel cell electric vehicle. To save time and cost, the digital vehicle model created in Matlab/Simulink and validated with real-world driving data is the main platform of the optimization study. The digital vehicle model was run with the minimum and maximum battery charge states determined by the Bald Eagle Search Algorithm, and hydrogen consumption and battery cycle values were obtained. By using the algorithm and digital vehicle model together, hydrogen consumption was minimized and range was increased. It was aimed to extend the life of the parts by considering the battery cycle. At the same time, the number of battery packs was included in the optimization and its effect on consumption was investigated. According to the study results, the total hydrogen consumption of the fuel cell electric vehicle decreased by 57.8% in the hybrid driving condition, 23.3% with two battery packs, and 36.27% with three battery packs in the constant speed driving condition. Full article

The energy consumption profiles of conventional fuelled and electric vehicles are different due to the fundamental differences in the driving characteristics of these vehicles, which have been actively researched elsewhere but mostly on the basis of uncommon geographical contexts. This study, therefore, collected driving data on electric and conventional diesel buses running along exactly the same set of bus routes in Hong Kong during normal daily revenue operations. This enabled a fair comparison of driving characteristics for both types of bus under identical real-life, on-road driving conditions, which highlighted the originality and contributions of this study. A three-step approach was adopted to carry out detailed driving pattern analyses, which included key driving parameters, speed–acceleration probability distributions (SAPDs), and vehicle-specific power (VSP) distributions. Results found that route-based comparisons did highlight important differences in driving patterns between electric and diesel buses that might have been smoothed out by analyses with mixed-route datasets. In particular, the spread, intensity, and directions of these differences were found to be exaggerated at the route-based level. The differences in driving patterns varied across different routes, which has significant implications on vehicle energy consumption. Government agencies and/or bus operators should make references to these results in formulating electric bus deployment plans. Full article

Although the EVRP (Electric Vehicle routing problem) has promising results on the environmental scale, its implementation has proved challenging. The difficulty of the EVRP resides in the limited driving range of the electric vehicles, combined with the significant charging time. While the charging cost is less than the cost of fuel, this charge time adds to the overall travel time and may overlap with customers’ time windows. All these factors increased the computational time exponentially and resulted in the need to overlook some constraints such as traffic congestion, road conditions, weather impact on energy consumption, and driving style, to name a few, in order to speed up execution time. While this method proved effective in accelerating the process of the EVRP, it did, however, render the approach unrealistic, as it steered far from real-life settings and made the approach unpredictable when facing dynamic and changing parameters. In this paper, we try to remedy this issue by proposing an approach in which we try to replicate real-life parameters such as heterogenous fleets, energy consumption, and infrastructure data. The objective of our approach was to minimize the total travel time, travel distance, energy consumed, and the number of vehicles deployed. To solve this problem, we propose a three-stages approach, in which the first stage consists of a newly developed dispatching approach where customers are assigned to vehicles. The second stage uses the genetic algorithm to find a set of optimal paths, and, finally, in the third stage, charging stations are inserted in the selected paths. Upon testing our approach on Solomon’s instances, our approach proved effective in finding optimal solutions in a reasonable time for five- to fifteen-customer datasets. However, when trying to solve larger datasets, the approach was slowed down by the extreme number of constraints it had to satisfy. Full article

Interior permanent magnet synchronous motors (IPMSMs) are extensively used as traction motors today because of their exceptional torque, power density, and wide, constant power operating range. Under real-world usage, an IPMSM rotor undergoes varying electromagnetic, thermal, and mechanical loads. Under such conditions, fatigue life-based design criteria should be used over stress-based design criteria to ensure the structural integrity of the rotor. Moreover, the driving dynamics can change the rotor temperature continuously, which affects the electromagnetic, mechanical, and fatigue properties of the rotor material. This paper proposes a robust thermomechanical rotor fatigue simulation workflow considering significant loads acting on an IPMSM rotor and the temperature variation throughout a drive cycle. It discusses an accelerated fatigue life estimation approach based on the peak valley extraction method to reduce the simulation time significantly for the stress and fatigue analysis. Then, it presents a method for a stress-life curve generation for variable loading. It also presents a sensitivity study with a median S-N curve, and a 90% reliability and 95% confidence (R90C95) S-N curve. Full article

The subject of the article was the chemical analysis of gasoline and exhaust gas samples taken from an urban two-wheeled vehicle. The main aim of the work was to identify chemical compounds emitted by a group of urban two-wheeled vehicles depending on the engine’s operating parameters. First, engine operating parameters and driving parameters of three urban two-wheeled vehicles were measured in real operating conditions. Based on the averaged results, engine operating points were determined for exhaust gas samples that were collected into Tedlar bags. The exhaust gas composition of individual chemical substances obtained in the chromatographic separation process were subjected to a detailed analysis relating the engine operating point with their emission rate, with each individual component being assessed in terms of its impact on human health. The obtained qualitative analysis results indicated the presence of alkenes, alkanes, aliphatic aldehydes, and aromatic and cyclic hydrocarbons (cycloalkanes) in the tested samples. The experiments provided a variety of conclusions relating to the operating parameters of a two-wheeler engine. Qualitative assessment of exhaust samples showed that a two-wheeled vehicle was characterized by the most varying composition of BTX aromatic hydrocarbons derivatives, which are particularly dangerous to human health and life. Therefore, the authors suggest that in the future, approval procedures regarding toxic emissions should be extended to include chromatographic tests. The presented results are an extension of previous studies on toxic emissions from urban two-wheeled vehicles in real operating conditions that were published in other journals. Full article

The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively. Full article

The use of hydrogen fuel produced from renewable energy sources is an effective way to reduce well-to-wheel CO₂ emissions from automobiles. In this study, the performance of a hydrogen-powered series hybrid vehicle was compared with that of other powertrains, such as gasoline-powered hybrid, fuel cell, and electric vehicles, in a simulation that could estimate CO₂ emissions under real-world driving conditions. The average fuel consumption of the hydrogen-powered series hybrid vehicle exceeded that of the gasoline-powered series hybrid vehicle under all conditions and was better than that of the fuel cell vehicle under urban and winding conditions with frequent acceleration and deceleration. The driving range was longer than that of the battery-powered vehicle but approximately 60% of that of the gasoline-powered series hybrid. Regarding the life-cycle assessment of CO₂ emissions, fuel cell and electric vehicles emitted more CO₂ during the manufacturing process. Regarding fuel production, CO₂ emissions from hydrogen and electric vehicles depend on the energy source. However, in the future, this problem can be solved by using carbon-free energy sources for fuel production. Therefore, hydrogen-powered series hybrid vehicles show a high potential to be environmentally friendly alternative fuel vehicles. Full article