1,721,004 research outputs found
Route Generation Methodology for Energy Efficiency Evaluation of Connected and Automated Vehicles
Full text linkEvaluation of the energy savings potential of Connected and Automated Vehicles (CAVs) technologies necessitates a representative baseline that accounts for the inherent variability due to route, terrain, traffic, traffic lights, etc., in real-world driving conditions. While considerable work has been done in the field of optimal energy management, eco-driving and eco-routing of CAVs, few contributions have addressed the creation of a representative baseline to realistically evaluate the energy savings potential of these technologies. This work proposes a route generation methodology based on leveraging a high-dimension driving dataset to construct diverse subset of synthetic driving trips and synthetic routes for large scale evaluation of energy consumption of CAVs. The generated synthetic routes can then be used to extract real-world routes from open-source mapping platforms, which have similar characteristics as the generated synthetic routes
Sliding mode control of spatial mechanical systems decoupling translation and rotation
Full text linkThis paper looks at the robust trajectory control of spatial mechanical systems using sliding mode techniques. Two distinctions of the proposed method from reported methods are: (1) The measure of attitudinal error used is intrinsically defined, Euclidean-geometric, and intuitive. From Euler's theorem it follows that given a desired and actual attitude of a rigid body there exists an axis and angle of rotation relating the two attitudes. This defines a relative rotation vector, which is used as an intrinsically defined, intuitive measure of error. Reported methods use algebraic differences of entities such as generalized coordinates representing attitude. While functionally correlated to attitudinal error, these measures are not intrinsically defined. (2) A novel, dynamically nonlinear sliding function is used that results in a simple control law. The parameters of this function are dynamically and geometrically intuitive. Simulation results are given for a spacecraft tracking a complex desired trajectory
Electrifying Vocational Trucks: An Overview of Advancements and Challenges in Electric and Hybrid Powertrains with Emphasis on Auxiliary Power Considerations
No full textElectric and hybrid powertrains are steadily gaining popularity, showcasing their efficacy in reducing greenhouse gas emissions and pollution, particularly in urban environments. This also applies to medium and heavy-duty vocational trucks. Truck manufacturers have been expanding their electrified portfolio and some of them have already announced their plans to phase out fossil fuels. Vocational trucks are essential for the industry of commercial vehicles, represent an extremely heterogeneous class, and are often upfitted by third-party companies. In general, vocational trucks are designed for specific jobs. Typically, they are driven on short routes, but they may work for longer hours in comparison to freight transportation vehicles. Most importantly, among the broad category of vocational trucks, some vehicles greatly exploit power take-offs to drive auxiliary systems, like refuse trucks, utility trucks, cement trucks, and sweeper trucks. The benefits resulting from the kinetic energy regeneration of urban driving are undeniable and well-documented in several studies. Electrified short-haul, delivery, and box vocational trucks are indeed becoming widely accepted, but the auxiliaries of these trucks do not need a significant amount of energy. In this regard, few studies analyze the effect of auxiliary loads, even if they may require significant power and cause considerable drifts in energy consumption estimation. Indeed, velocity-based driving cycles are commonly used to assess the energy savings of electrified powertrains, but they only focus on analyzing the power needed for traction purposes. By focusing on vocational trucks that greatly rely on power take-offs and auxiliary power flows, this study shows an overview of the existing advancements in electric and hybrid vocational trucks, drawing on both academic and industrial instances, while explaining their different needs. Finally, this study analyzes efforts from both academia and industry to generate standardized duty cycles that can be used to assess the energy consumption of medium and heavy-duty vocational trucks, avoiding the need to replicate unique duty cycles that are measured on specific vehicles and environments
Intelligent energy flow management of a nanogrid fast charging station equipped with second life batteries
No full textIn this paper we investigate a public Fast Charge (FC) station nanogrid equipped with a Photovoltaic (PV) system and an Energy Storage System (ESS) using second-life Electric Vehicle (EV) batteries. Since the nanogrid is intended for installation in urban areas, it is designed with a very limited connection with the grid to assure peak shaving and encourage PV autoconsumption. To demostrate the effectiveness of this approach to FC stations, an Energy Management System (EMS) is developed to manage the energy demand uncertainty of EVs and the power gap between the grid connection and the FC service. In particular, we propose a machine learning procedure for the automatic synthesis of a suitable (fuzzy) rule-based EMS. Indeed, we posit that a prediction based EMS would result not effective because of the stochastic and intermittent behavior of the FC load, and that a crisp rule-based system, defined by expert knowledge, would be too limited to capture uncertain behavior. The concept is demonstrated in a simulated environment inspired by the “Smart Columbus” project, implementing a mixed deterministic-stochastic process to simulate EV energy demand. In particular, different EV fleets and PV sizes are considered for EMS training, offering insights into the optimal size of PV system and nanogrid system effectiveness. The proposed approach is evaluated by comparing the EMS performance with related optimal benchmark solutions, evaluated by considering known a priori the overall PV and FC demand. The results show that the EMS performance is approximately within 10% of the benchmark optimal value
Effect of Engine Start and Clutch Slip Losses on the Energy Management Problem of a Hybrid DCT Powertrain
Full text linkA Dynamic Programming (DP) formulation is developed to find the global optimal solution to the energy management of a parallel Plug-in Hybrid Electric Vehicle (PHEV) equipped with a Dual-Clutch Transmission (DCT). The effects of integrating in the DP formulation the losses accounting for gearshifts and engine starts are studied in terms of the overall fuel consumption; the optimal control solutions obtained depends on the occurrence of these transient events. These sources of dissipation are modeled through physical considerations thus enabling the DP algorithm to decide when it is more convenient, in terms of minimizing the total energy consumption, to perform either a gearshift or an engine start. This capability differentiates the DP formulation here presented from those presented in previous studies
A novel mechanical analogy based battery model for SoC estimation using a multi-cell EKF
No full textThe future evolution of technological systems dedicated to improve energy efficiency will strongly depend on effective and reliable Energy Storage Systems, as key components for Smart Grids, microgrids and electric mobility. Besides possible improvements in chemical materials and cells design, the Battery Management System is the most important electronic device that improves the reliability of a battery pack. In fact, a precise State of Charge (SoC) estimation allows the energy flows controller to exploit better the full capacity of each cell. In this paper, we propose an alternative definition for the SoC, explaining the rationales by a mechanical analogy. We introduce a novel cell model, conceived as a series of three electric dipoles, together with a procedure for parameters estimation relying only on voltage measures and a given current profile. The three dipoles represent the quasi-stationary, the dynamic and the instantaneous components of voltage measures. An Extended Kalman Filter (EKF) is adopted as a nonlinear state estimator. Moreover, we propose a multi-cell EKF system based on a round-robin approach to allow the same processing block to keep track of many cells at the same time. Performance tests with a prototype battery pack composed by 18 A123 cells connected in series show encouraging results.The future evolution of technological systems dedicated to improve energy efficiency will strongly depend on effective and reliable Energy Storage Systems, as key components for Smart Grids, microgrids and electric mobility. Besides possible improvements in chemical materials and cells design, the Battery Management System is the most important electronic device that improves the reliability of a battery pack. In fact, a precise State of Charge (SoC) estimation allows the energy flows controller to exploit better the full capacity of each cell. In this paper, we propose an alternative definition for the SoC, explaining the rationales by a mechanical analogy. We introduce a novel cell model, conceived as a series of three electric dipoles, together with a procedure for parameters estimation relying only on voltage measures and a given current profile. The three dipoles represent the quasi-stationary, the dynamic and the instantaneous components of voltage measures. An Extended Kalman Filter (EKF) is adopted as a nonlinear state estimator. Moreover, we propose a multi-cell EKF system based on a round-robin approach to allow the same processing block to keep track of many cells at the same time. Performance tests with a prototype battery pack composed by 18 A123 cells connected in series show encouraging results
Modeling and Simulation of Residential Power Demand Including Transportation
No full textPersonal transportation has a significant impact on the residential electric energy usage due to the interaction of alternative fueled vehicles with the electric grid. This phenomenon is projected to grow significantly, as several studies confirm that the market penetration of alternative fueled vehicles will steadily increase in the future.
This paper presents a control-oriented model that predicts the daily residential power demand considering multiple energy carriers and different types of alternative fueled vehicles for personal transportation. The model has been used to perform an energy analysis on a large sample of homes with the objective of evaluating the impact of personal transportation on the residential electric power demand. Two penetration levels are considered in the study and the results are evaluated based on several metrics
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A multi-dimensional well-to-wheels analysis of passenger vehicles in different regions: Primary energy consumption, CO2 emissions, and economic cost
No full textThis paper proposes an exergy-based well-to-wheels analysis to compare different passenger vehicles, based on three key indicators: petroleum energy use, CO2 emissions, and economic cost. A set of fuel pathways, including petroleum-based fuels, compressed natural gas, biofuels, and electricity are considered in five representative national energy mixes, namely Brazil, China, France, Italy, and the United States of America. Results show no fundamental difference in the fossil fuel pathways among the five scenarios considered. Compressed natural gas vehicles and electric vehicles can completely displace oil consumption in the personal transportation sector. Compressed natural gas vehicles also reduce CO2 emissions by over 20% compared to gasoline vehicles. Emissions from electric vehicles greatly vary depending on the electricity mix. In low-carbon electricity mixes electric vehicles reach almost-zero CO2 emissions, while the use of biofuels leads to the lowest CO2 emissions in carbon-intensive electricity generation mixes, where vehicles running on E85 could reduce CO2 emission by over 50% compared to gasoline vehicles. Hybrid electric vehicles show the lowest overall economic cost, due to improved efficiency and low cost of petroleum-based fuels. Vehicles running on electricity are characterized by significantly higher capital cost and lower operating costs. Thus, different electricity generation costs impact minimally the overall cost. These results can be used to inform decision-makers regarding the multi-dimensional impact of passenger vehicles, including environmental impact, economic cost, and depletion of primary energy resources, with particular focus on petroleum
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