Search Results (14)

Energy management strategies (EMS) in the context of electric or hybrid vehicles can optimize the available energy by minimizing consumption. Most optimization-based EMS are not real-time-applicable for an accurate estimation of future consumption. The performance of these strategies also strongly depends on the driving patterns, which may be influenced by road and traffic conditions, among other factors such as driving style, weather, vehicle type, etc. The primary contribution of this work is to develop a novel two-layer driving pattern recognition (DPR) system for roadway type and traffic classification, thus enabling the identification of unknown patterns for the enhancement of the prediction of energy consumption of an electric vehicle (EV). The novelty of this work lies in the development of a strategy based on real-time data which is capable of classifying driving patterns and implementing an optimized EMS based on the results of the DPR. In the approach, first, labels are defined based on statistical features related to speed followed by the creation of representative driving patterns (RDPs). A neural network-based classifier is then employed for classification into six classes based on four features. A training accuracy of 97.7% is achieved with the classification of unknown speed profiles into the known RDPs. Testing with patterns from two different test routes shows an accuracy of 97.45% and 96.98% during morning and 96.65% and 94.12% during evening hours, respectively. Apart from the route and time of data collection, accuracy is also a function of sampling time horizon and the threshold values chosen for the features. A sensitivity analysis was also performed to evaluate the relative importance of each feature. An EMS based on sequential quadratic programming (SQP) was combined with DPR for the computation of optimal energy consumption. Simulation results show that maximum and minimum energy savings of 61% and 18% were obtained under suburban low traffic and highway high traffic conditions, respectively. An eco-driving or driver speed advisory system may further be developed based on information obtained from multiple routes and varying traffic scenarios. Full article

As the European Union advances its regulatory framework on energy efficiency, the introduction of an energy label for electric cars appears increasingly relevant. Anticipating this policy development, we present a scoping analysis of energy consumption and efficiency trade-offs across 342 fully electric cars available in Europe. Our results suggest that certified and real-world energy consumption average 19 ± 4 kWh/100 km and 21 ± 4 kWh/100 km, translating into drive ranges of 440 ± 120 km and 380 ± 110 km, respectively. Energy consumption is correlated with mass, frontal area, and battery capacity but less so with rated power and vehicle price. Each 100 kg of vehicle mass and 0.1 m² of frontal area increases energy consumption by 0.2 ± 0.1 kWh/100 km and 0.9 ± 0.1 kWh/100 km, respectively. Raising battery capacity by 10 kWh elevates vehicle mass by 143 ± 4 kg, energy consumption by 0.6 ± 0.1 kWh/100 km, drive range by 44 ± 2 km, and vehicle price by 12,000 ± 600 EUR. Efficient cars are available at any price, but long drive ranges have a cost. These findings point to considerable efficiency trade-offs that could be revealed to consumers through a dedicated energy label. We propose several options for classifying vehicles on an efficiency scale from A to G, with and without drive range and battery capacity as utility parameters. Our analysis provides a rationale for the energy labeling of electric cars in the European Union and could inspire similar analyses for other vehicle categories such as e-scooters, lightweight electric three- and four-wheelers, e-busses, e-trucks, and electric non-road machinery. Full article

This paper reports on collaborative activities to promote energy- and resource-efficient construction practices in Vietnam. First, the governance framework was introduced, including government decrees and technical standards. Then, a laboratory with building physics measurement technology was designed and partly set up at the local partner, the Vietnam Institute for Building Materials (VIBM). This can be used to determine the essential characteristic values required for the implementation of energy standards. The requirements of the national technical regulation on energy-efficient buildings of Vietnam—QCVN09:2017/BXD—form the basis for the prioritization of characteristic values. Furthermore, the description of basic characteristic values from international standards can also be used for calculations to optimize the energy consumption of buildings. To carry out transient hygrothermal computer simulations, special characteristic values are also included. These are particularly useful for the research and development of new building materials and the evaluation of entire buildings in terms of thermal and moisture protection. In this way, the practical means for implementing governance instruments are provided, and the associated technical applications are supported. Based on the example of Vietnam, this paper indicates how a developing country can develop a road map for improving its systems for testing, rating, and labeling building materials for energy performance towards sustainable development. Full article

Vegetation encroachment in power line corridors has multiple problems for modern energy-dependent societies. Failures due to the contact between power lines and vegetation can result in power outages and millions of dollars in losses. To address this problem, UAVs have emerged as a promising solution due to their ability to quickly and affordably monitor long corridors through autonomous flights or being remotely piloted. However, the extensive and manual task that requires analyzing every image acquired by the UAVs when searching for the existence of vegetation encroachment has led many authors to propose the use of Deep Learning to automate the detection process. Despite the advantages of using a combination of UAV imagery and Deep Learning, there is currently a lack of datasets that help to train Deep Learning models for this specific problem. This paper presents a dataset for the semantic segmentation of vegetation encroachment in power line corridors. RGB orthomosaics were obtained for a rural road area using a commercial UAV. The dataset is composed of pairs of tessellated RGB images, coming from the orthomosaic and corresponding multi-color masks representing three different classes: vegetation, power lines, and the background. A detailed description of the image acquisition process is provided, as well as the labeling task and the data augmentation techniques, among other relevant details to produce the dataset. Researchers would benefit from using the proposed dataset by developing and improving strategies for vegetation encroachment monitoring using UAVs and Deep Learning. Full article

Road lighting is one of the largest consumers of electric energy in cities. Research into energy-saving street lighting is of great significance to city sustainable development and economies, especially given that many countries are now in a period of energy shortage. The control system is critical for energy-saving street lighting, due to its capability to directly change output power. Here, we propose a control system with high intelligence and efficiency, by incorporating improved YOLOv5s with terminal embedded devices and designing a new dimming method. The improved YOLOv5s has more balanced performance in both detection accuracy and detection speed compared to other state-of-the-art detection models, and achieved the highest cognition recall of 67.94%, precision of 81.28%, 74.53%AP${}_{50}$, and frames per second (FPS) of 59 in the DAIR-V2X dataset. The proposed method achieves highly complete and intelligent dimming control based on the prediction labels of the improved YOLOv5s, and a high energy-saving efficiency was achieved during a two week-long lighting experiment. Furthermore, this system can also contribute to the construction of the Internet of Things, smart cities, and urban security. The proposed control system here offered a novel, high-performance, adaptable, and economical solution to road lighting. Full article

The road transportation sector is a dominant and growing energy consumer. Although investigations to quantify the road infrastructure’s impact on energy consumption have been carried out, there are currently no standard methods to measure or label the energy efficiency of road networks. Consequently, road agencies and operators are limited to restricted types of data when managing the road network. Moreover, initiatives meant to reduce energy consumption cannot be measured and quantified. This work is, therefore, motivated by the desire to provide road agencies with a road energy efficiency monitoring concept that can provide frequent measurements over large areas across all weather conditions. The proposed system is based on measurements from in-vehicle sensors. The measurements are collected onboard with an Internet-of-Things (IoT) device, then transmitted periodically before being processed, normalized, and saved in a database. The normalization procedure involves modeling the vehicle’s primary driving resistances in the driving direction. It is hypothesized that the energy remaining after normalization holds information about wind conditions, vehicle-related inefficiencies, and the physical condition of the road. The new method was first validated utilizing a limited dataset of vehicles driving at a constant speed on a short highway section. Next, the method was applied to data obtained from ten nominally identical electric cars driven over highways and urban roads. The normalized energy was compared with road roughness measurements collected by a standard road profilometer. The average measured energy consumption was 1.55 Wh per 10 m. The average normalized energy consumption was 0.13 and 0.37 Wh per 10 m for highways and urban roads, respectively. A correlation analysis showed that normalized energy consumption was positively correlated to road roughness. The average Pearson correlation coefficient was 0.88 for aggregated data and 0.32 and 0.39 for 1000-m road sections on highways and urban roads, respectively. An increase in IRI of 1 m/km resulted in a 3.4% increase in normalized energy consumption. The results show that the normalized energy holds information about the road roughness. Thus, considering the emergence of connected vehicle technologies, the method seems promising and can potentially be used as a platform for future large-scale road energy efficiency monitoring. Full article

(1) Background: Transition to smart cities involves many actions in different fields of activity, such as economy, environment, energy, government, education, living and health, safety and security, and mobility. Environment and mobility are very important in terms of ensuring a good living in urban areas. Considering such arguments, this paper proposes monitoring and mapping of a 3D traffic-generated urban noise emissions using a simple, UAV-based, and low-cost solution. (2) Methods: The collection of relevant sound recordings is performed via a UAV-borne set of microphones, designed in a specific array configuration. Post-measurement data processing is performed to filter unwanted sound and vibrations produced by the UAV rotors. Collected noise information is location- and altitude-labeled to ensure a relevant 3D profile of data. (3) Results: Field measurements of sound levels in different directions and altitudes are presented in the paperwork. (4) Conclusions: The solution of employing UAV for environmental noise mapping results in being minimally invasive, low-cost, and effective in terms of rapidly producing environmental noise pollution maps for reports and future improvements in road infrastructure. Full article

A major advantage of online organic produce shopping is the fact that it saves energy and reduces emissions otherwise generated by customers during their time spent on the road and while shopping. Organic products in general positively impact sustainability, the environment, and the regions of their origin along with the social changes in these regions and further rural development. Moreover, these products positively impact the perceived health benefits and quality of food labeled as organic. The Czech Republic has currently seen a rise in organic food purchasing and supply trends. This study maps the factors possibly influencing consumers’ decision to go shopping for organic food online. Observed factors include the following demographic characteristics of consumers (respondents): gender, age, education, household income, number of children in the household and number of household members. A total of 757 respondents from the Czech Republic from September 2020 to December 2020 took part in the research. Logistic regression, used for data processing, identified the statistically significant effects of education, income and number of household members on online purchases. These conclusions were confirmed by a detailed contingency tables analysis, including the almost monotonous trend of the dependencies, with only minor deviations in a maximum of one category. The strongest influence of some categories on the emergence of partial dependencies was found by residue analysis. The research confirmed that the frequency of online grocery shopping increases significantly with increasing education and income of respondents and decreases with increasing the number of household members. Most respondents apparently shop for groceries online because of time savings, better product choice and more convenient and easier search. Full article

Introduction: Currently Europe regards itself as a leader in the global race towards smart automated transport. According to ERTRAC, European Road Transport Research Advisory Council, automated driving innovation is motivated by technological advancements as well as “social goals of equality”. This article analyzes to what extent such dimensions of gender and diversity have become visible in smart car advertisements and how they correspond with the notion of Gender-Smart Mobility, which signifies equal and accessible transport solutions. Methods: Guided by theoretical notions of gender scripts and discourse analysis, this article addresses how perspectives of smart technology, gender, and class are carved out and handled in YouTube videos applied as marketing tools. Using visual analysis as a method, videos from well-known car producers such as BMW and Volvo are scrutinized. The visual analysis includes a presentation of the car company, descriptions of the most relevant YouTube videos, and discussion of the findings. Results: The visual analysis of the Volvo and BMW YouTube videos points to the lack of inclusiveness. There continues to be a prevalent reproduction of gendered stereotypes in the videos, not least in the notion of ‘hyper masculinity’ storytelling by BMW and how leaders (be they women or men) look, i.e., middle-class people. Volvo, on the other hand, has maintained its focus on female professionals in parallel with the introduction of new and energy-saving cars. Yet, a rather one-sided presentation of a professional business-woman is depicted as a replication of the businessman. Conclusion: In the final section, it is assessed how the visual branding complies with the notion of Gender-Smart Mobility, a concept that was developed in the EU Horizon 2020 project TInnGO. The two brands meet the Gender-Smart Mobility indicator, but only to some degree. None of the companies are fully inclusive, and it is difficult to label them as gender-smart and sustainable despite their ambitions of feeding into the green transition. Full article

Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time–frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car’s dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates. Full article

A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation. Full article

Sustainability has been recognized as a major concern globally since the Brudtland Report, in 1987, and further reinforced in 2015 by the United Nations Sustainable Development Goals (UNSDG) 2030. This paper reviews the methodologies and criteria of sustainability applied to fashion products, regarding products’ environmental footprint (environmental life cycle assessment/analysis; e-LCA), the social issues (including the social life cycle assessment/analysis; s-LCA) and the transparency in reporting sustainability. In our review we seek KPIs (key performance indicators) that allow classification of a pair of shoes or a piece of cloth on a scale from A to E, i.e., products can be compared with a benchmark and classified accordingly with a simple labelling scheme, which is easily understandable by the consumers. This approach is similar to those used to classify electrical appliances, housing energy consumption for thermal comfort, food Nutri-Scores, CO₂ levels of road vehicles, and tire performance. In this review we aim to identify the initiatives and measures being put into practice by the top global fashion brands. We found that, despite the existence of GRI (global sustainability reporting initiative) standard reporting, most companies follow their own methods or others created within the industry rather than those created in the scientific community. Examples include the Higg index, the Transparency Index, and the Social Codes of Conduct (CoC). In this study, we conducted an extensive review of certification schemes and labels already applied to fashion products, and identified a multitude of labels and lack of harmonization in communicating sustainability. As result, we compiled a summary table of all criteria, methodologies, and possible KPIs that can be considered the basis for a benchmark and score of a fashion product. This topic is crucial to avoid “green washing” and a lack of transparency for the buyer’s community, i.e., business to consumer (B2C), and for the business community, i.e., business to business (B2B) relationships, which comprise a complex multi-layer supply chain of suppliers and sub-suppliers. The UNSDG 2030 “Responsible Consumption and Production” frames these efforts to facilitate standardization of KPIs in terms of structure, criteria, and their measurement. The most common KPI is environmental global warming impact (expressed as CO_2eq) based on life cycle assessment/analysis (LCA) principles (established in 2000), which provide an appropriate base to monitor and benchmark products. However, in our innovative review of t-shirt e-LCA, we identified a wide range of e-LCA assumptions, relating to different boundaries, allocations, functional units, and impact categories, which represent a major challenge in benchmarking. Full article

A vehicle’s air drag coefficient (Cd) and rolling resistance coefficient (RRC) have a significant impact on its fuel consumption. Consequently, these properties are required as input for the certification of the vehicle’s fuel consumption and Carbon Dioxide emissions, regardless of whether the certification is done via simulation or chassis dyno testing. They can be determined through dedicated measurements, such as a drum test for the tire’s rolling resistance coefficient and constant speed test (EU) or coast down test (US) for the body’s air Cd. In this paper, a methodology that allows determining the vehicle’s Cd·A (the product of Cd and frontal area of the vehicle) from on-road tests is presented. The possibility to measure these properties during an on-road test, without the need for a test track, enables third parties to verify the certified vehicle properties in order to preselect vehicle for further regulatory testing. On-road tests were performed with three heavy-duty vehicles, two lorries, and a coach, over different routes. Vehicles were instrumented with wheel torque sensors, wheel speed sensors, a GPS device, and a fuel flow sensor. Cd·A of each vehicle is determined from the test data with the proposed methodology and validated against their certified value. The methodology presents satisfactory repeatability with the error ranging from −21 to 5% and averaging approximately −6.8%. A sensitivity analysis demonstrates the possibility of using the tire energy efficiency label instead of the measured RRC to determine the air drag coefficient. Finally, on-road tests were simulated in the Vehicle Energy Consumption Calculation Tool with the obtained parameters, and the average difference in fuel consumption was found to be 2%. Full article

High-resolution visible remote sensing imagery and thermal infrared hyperspectral imagery are potential data sources for land-cover classification. In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-cover classification method based on the fusion of visible and thermal infrared hyperspectral imagery is proposed, namely, SSECRF (spatial-spectral-emissivity land-cover classification based on conditional random fields). A spectral-spatial feature set is constructed considering the spectral variability and spatial-contextual information, to extract features from the high-resolution visible image. The emissivity is retrieved from the thermal infrared hyperspectral image by the FLAASH-IR algorithm and firstly introduced in the fusion of the visible and thermal infrared hyperspectral imagery; also, the emissivity is utilized in SSECRF, which contributes to improving the identification of man-made objects, such as roads and roofs. To complete the land-cover classification, the spatial-spectral feature set and emissivity are integrated by constructing the SSECRF energy function, which relates labels to the spatial-spectral-emissivity features, to obtain an improved classification result. The classification map performs a good result in distinguishing some certain classes, such as roads and bare soil. Also, the experimental results show that the proposed SSECRF algorithm efficiently integrates the spatial, spectral, and emissivity information and performs better than the traditional methods using raw radiance from thermal infrared hyperspectral imagery data, with a kappa value of 0.9137. Full article