1,720,980 research outputs found
Battery state of health estimation with improved generalization using parallel layer extreme learning machine
Full text linkThe online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 μs in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications
A Method for Battery Sizing in Parallel P4 Mild Hybrid Electric Vehicles
Full text linkThis article deals with a sensitivity analysis concerning the influence that the capacity of the battery in a parallel hybrid powertrain has on the vehicle's energy regeneration. The architecture under analysis is constituted by an internal combustion engine (ICE), which provides traction to the front axle's wheels, and an electric motor powering the rear wheels. The energy management system (EMS) is based on a simple torque split strategy that distributes the driver's required torque between the front and rear machines as a function of battery and electric motor functional limitations (state of charge, temperatures, and maximum admissible currents). Together with the selected driving cycles, the central role played by the battery size in the overall vehicle recoverable energy is evaluated, while the influence of the powertrain limitations is highlighted, accounting both for uncertain parameters (e.g., initial state of charge [SoC 0]) and for tunable parameters (e.g., maximum electric traction vehicle speed). Therefore, a method of sizing the battery of a P4 mild hybrid electric vehicle (HEV), which allows the maximization of the braking energy recovery, is developed
State of Health Estimation of Lithium‐Ion Batteries in Electric Vehicles under Dynamic Load Conditions
Full text linkAmong numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by predicting battery health conditions. The challenge of accurate estimation of SOH is based on the uncertain dynamic operating condition of the EVs and the complex nonlinear electrochemical characteristics exhibited by the lithium‐ion battery. This paper presents an artificial neural network (ANN) classifier experimentally validated for the SOH estimation of lithium‐ion batteries. The ANN‐based classifier model is trained experimentally at room temperature under dynamic variable load conditions. Based on SOH characterization, the training is done using features such as the relative values of voltage, state of charge (SOC), state of energy (SOE) across a buffer, and the instantaneous states of SOC and SOE. At implementation, due to the slow dynamics of SOH, the algorithm is triggered on a large‐scale periodicity to extract these features into buffers. The features are then applied as input to the trained model for SOH estimation. The classifier is validated experimentally under dynamic varying load, constant load, and step load conditions. The model accuracies for validation data are 96.2%, 96.6%, and 93.8% for the respective load conditions. It is further demonstrated that the model can be applied on multiple cell types of similar specifications with an accuracy of about 96.7%. The performance of the model analyzed with the confusion matrices is consistent with the requirements of the automotive industry. The classifier was tested on a Texas F28379D microcontroller unit (MCU) board. The result shows that an average real‐time execution speed of 8.34 μs is possible with a negligible memory occupation
Performance comparison between electromechanical and electro-hydrostatic regenerative shock absorbers
Full text linkGoing 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
Considerazioni sulla sinostosi della sutura mesiopalatina nell'uomo:studio istologico
No full textThe authors carried out an histologic study on bioptic tissue coming from the human midpalatal suture,for the sake of investigate about the suture's evolutive condition.The results showed the absence of synostosis in men about 30 years,so the orthopedic action of Rapid Maxillary expansion can be considered also in adult age
Modeling and evaluation of damping coefficient of eddy current dampers in rotordynamic applications
No full textEddy current dampers (ECD) can be used to introduce damping in rotordynamic applications. ECDs are contactless in nature and can be made to introduce negligible drag force, thus being a perfect match for passive magnetic bearings such as permanent magnet bearings and superconducting bearings. However, modeling and estimating the amount of damping introduced by an ECD is a difficult task due to complicated geometry and working conditions.
The present study presents a novel method for modeling and identification of the damping characteristics of ECDs for rotordynamic applications. The proposed method employs an analytical dynamic model of the ECD and curve fitting with results of electromagnetic finite element (FE) models to obtain the parameters characterizing the ECD׳s mechanical impedance. The damping coefficient can be obtained with great accuracy from a single FE solution in quasistatic conditions. The validity of the proposed method is limited to the case of ECDs employing an axisymmetric conductor, such as a disc or a cylinder, thus covering most cases in rotordynamic applications. Finally, the accuracy of the identification procedure is verified experimentally by comparing the model׳s results with experimental tests
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
- …
