Model and design of a double frequency piezoelectric resonator

A novel design of a multifrequency mechanical resonator with piezoelectric materials for energy harvesting is presented. The electromechanical response is described by a finite element model, which predicts the output voltage and the generated powe

An explicit construction of ruled surfaces

The main goal of this paper is to give a general algorithm to compute, via computer-algebra systems, an explicit set of generators of the ideals of the projective embeddings of ruled surfaces, i.e. projectivizations of rank two vector bundles over curves, such that the fibers are embedded as smooth rational curves. There are two different applications of our algorithm. Firstly, given a very ample linear system on an abstract ruled Surface, our algorithm allows computing the ideal of the embedded surface, all the syzygies, and all the algebraic invariants which are computable from its ideal as, for instance, the k-regularity. Secondly, it is possible to prove the existence of new embeddings of ruled surfaces, The method can be implemented over any computer-algebra system able to deal with commutative algebra and Grobner-basis computations. An implementation of our algorithms for the computer-algebra system Macaulay2 (cf. [Daniel R. Grayson, Michael E. Stillman, Macaulay 2, a software system for research in algebraic geometry, 1993. Available at http://www.math.uiuc.edu/Macaulay2/]) and explicit examples are enclosed

Battery state of health estimation with improved generalization using parallel layer extreme learning machine

The 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

State of Health Estimation of Lithium‐Ion Batteries in Electric Vehicles under Dynamic Load Conditions

Among 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

Test and Theory of Electrodynamic Bearings Coupled to Active Magnetic Dampers

Electrodynamic bearings (EDBs) are passive magnetic bearings that exploit the interaction between eddy currents developed in a rotating conductor and a static magnetic field to generate forces. Similar to other types of magnetic suspensions, EDBs provide contactless support, thus avoiding problems with lubrication, friction and wear. Electrodynamic bearings have also drawbacks such as the difficulty in insuring a stable levitation in a wide speed range. The paper presents a solution where the EDBs are coupled with active magnetic dampers (AMDs) to guarantee a stable levitation

A Method for Battery Sizing in Parallel P4 Mild Hybrid Electric Vehicles

This 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

A multi-purpose control and power electronic architecture for active magnetic actuators

This paper shows the results related with the design and implementation of a multi-purpose electronic architecture used to drive magnetic actuators by means of a three-phase independent-legs module in place of the commonly used H-bridge modules. The typical application is the magnetic actuators drive used in active magnetic bearings. The architecture is composed of a control unit with a floating point Digital Signal Processor (DSP), a power board with six independent phase legs and a carrier board to interconnect them. When more than one module is required by the application, the communication between them is guaranteed by means of CAN bus interconnection. The proposed system allows to drive two pairs of opposite electromagnets, such as those typically used to control active magnetic bearings. The study is motivated by the opportunity of reducing the amount of power and control electronic components resulting in a more straightforward, efficient and cost reduction design

Non-dimensional design approach for electrodynamic bearings

Electrodynamic bearings (EDBs) are passive magnetic bearings that exploit the interaction between eddy currents developed in a rotating conductor and a static magnetic field to generate forces. Similar to other types of magnetic suspensions, EDBs provide contactless support, thus avoiding problems with lubrication, friction and wear. The most interesting aspect of EDBs is that levitation can be obtained by passive means, hence, no electronic equipment, such as power electronics or sensors, are necessary. Despite their promising characteristics, rotors running on EDBs are still lacking a design procedure; furthermore, at present the static behavior of a bearing can only be defined by means of finite element analyses. The aim of the present paper is to present a methodology that allows performing a first approximation design without resorting to detailed FE analyses. The methodology is based on the use of non-dimensional parameters, similar to the analysis of fluid bearings (Sommerfeld number). The non-dimensional quantities are derived using dimensional analysis, and contain the main geometrical and physical parameters determining the EDBs' performance. The relation between the non-dimensional quantities characterizing the static performance of the EDB is derived using FE simulations and is presented in the form of graphs

Model and design of a double frequency piezoelectric resonator

A novel design of a multifrequency mechanical resonator with piezoelectric materials for energy harvesting is presented. The electromechanical response is described by a finite element model, which predicts the output voltage and the generated power