The tubular linear SR machines were successfully tested in medical applications as artificial heart pump actuators [12].However, analysis and design of SR machines is a complex task compounded by their non-linear behavior. Despite some effort [13] the analysis and design calculations have not yet been developed into intuitive analytical tools comparable to the methods available for the more established types of machines, such as induction or permanent magnet. The main difficulty with the analysis and design of SR machines is the magnetic nonlinearity caused by the heavily saturated iron parts of the machine circuit. The non-saturating SR machines, as used in some niche applications, are not considered here.Given the wide variety of topological arrangements of SR machines, which is expected to grow in the future as the demand for the new applications increases [14], it is vital to establish computationally efficient analysis and design methods. The aim is to make the design task more systematic which in turn will open new application areas for the versatile SR electric machine technology.Reduced order computational methods, the most notable example being the magnetic equivalent circuit (MEC) approach, have been successfully employed in the past to various types of electric machines [15]. The main advantage of the MEC based models is that they are relatively accurate given their computational efficiency. The finite element analysis (FEA) is very useful for accurate analysis of the established electric machine technologies, but it does not offer the cause-and-effect insight when novel and unfamiliar machine topologies are being considered [16]. Therefore, bearing in mind the advantages of the MEC based analysis methods, the design cycle is proposed as illustrated in Fig. 1.The starting point (red rim) in Fig. 1 is where a novel topology SR machine is identified and considered for a certain application because it meets some particular requirements imposed by the application, for example: cost, volume and mass, mechanical, etc. Next, the improved flux tube based method [17], [18], which we propose in this paper, is employed to construct the electromagnetic model of the machine and is subsequently used in conjunction with a design search and optimization algorithm, e.g. the genetic algorithm (GA) [19]. Once a set of near optimal solutions
The magnetic design of Switched Reluctance (SR) motors is inherently a hierarchical process. The design cycle progresses through distinct stages where the accuracy improves but computing times increase greatly, thus it often becomes impractical to furnish extensive multi-objective optimization required to accomplish the optimal design. In order to enable rapid and accurate optimization of SR motors an improved reduced-order computational method of flux tubes is implemented to complement and practically replace the time consuming 2D finite element based magnetic analysis. The paper demonstrates how the use of the improved flux tubes approach to evaluate objective functions results in substantially faster while still accurate multi-objective optimization of SR motors. IntroductionSwitched Reluctance (SR) machines are often regarded as having the simplest mechanical design [1]- [3] compared to other conventional electric machines, such as permanent magnet dc, synchronous reluctance or induction, as they are brushless and have no permanent magnets (PM) or windings on the rotor. These design features make SR machines well suited to a wide range of applications where variable speed operation is required, such as general traction or pump drives. Moreover, the mechanical robustness of SR machines offers cheaper maintenance and better tolerance to harsh environments in which other types of machines cannot operate.Since SR motors are capable of operating in both constant torque and constant power regimes over a wide speed range [4], [5], this operating feature also makes them suitable for the automotive propulsion applications, where the absence of permanent magnets is considered necessary since this industry is very cost sensitive and insists on immunity from unforeseen material price fluctuations, as experienced by PM materials [6]. However, the absence of PM parts makes the SR machines much more difficult to control [7] due to their nonlinear torque-per-ampere characteristic. This operational nonlinearity is a result of the nonlinear magnetic characteristic as the machine's magnetic circuit becomes heavily saturated even during steady-state operation [8].Considering that SR machines are very nonlinear, they require complex analysis and robust design procedures to adequately predict their performance. Until now a rather limited success has been achieved in terms of accurate analytical design procedures for SR machines [9], [10] to provide satisfactory quantification of the machine performance. Most of the magnetic analysis and design is nowadays performed by numerical simulations based on a finite element method (FEM) in order to capture the detailed shape and allow for magnetic nonlinearity. The FEM based solutions can be accurate; however, they are time consuming and lack the intuitive insight into the cause-and-effect relationships between numerous machine design parameters. Moreover, the FEM solutions give only part of the
Switched Reluctance (SR) machines are magnetically non-linear electromechanical devices and their full and accurate performance prediction-especially of the instantaneous electromagnetic torque waveforms-is not amenable to closed form analytical solutions. Consequently, the analysis of such machines is usually performed with the aid of computer based numerical simulations that-even if providing good accuracy-do not offer physical insight into the electromagnetic energy conversion processes taking place inside the machine. For this reason, the SR machine design and analysis are not simple exercises as they require substantial computational resources and extensive prior design expertise. In this paper, a methodology for a reasonably accurate prediction of the instantaneous electromagnetic torque waveforms of the SR machines is proposed using a closed form analytical solution. The suggested approach relies on a simple vector analysis of the flux-linkage map of a non-linear SR machine and as such avoids integration, non-linear curve fitting or geometrical series summation. The proposed vector analysis based methodology offers intuitive physical insight into the electromagnetic energy conversion processes taking place inside the SR machine related to the instantaneous torque generation.
The prospect of physical exertion commonly acts as a deterrent to the adoption of cycling for everyday transport. A battery powered assistance torque electric motor could alleviate such physical exertion by reducing the effort required by the cyclist. This study investigates the potential effectiveness, efficiency, and energy saving of electrically-assisted cycling when assistance torque of a switched reluctance motor is designed to vary in accord to the cyclist instantaneous torque at the pedal cranks. Specifically, the modulated motor assistance torque is delivered at the least efficient human input torque points on the cycle. For a representative short distance cycling schedule modulating the instantaneous torque of the on-board electric motor causes the electric energy expenditure to not exceed that of the assisted cycling mode of an identical constant-torque motor. Furthermore, for the same speed profile cycling journey with added road gradient and head wind resistance, the energy expenditure of the modulated torque motor is equal to the constant torque motor. These findings indicate significant improvements in the cycling experience. INDEX TERMS modulated cycling torque, energy conversion, electric bike, switched reluctance machine, battery.
This paper proposes an integrated smart cycling system for assisted cycling, energy harvesting and wireless power transfer systems on a bicycle, an enabling platform for autonomous e-textiles-based sensing. The cyclist is assisted by a switched reluctance motor, which also acts as a switched reluctance generator that harvests a peak power of 7.5 W, at 10% efficiency during cycling to power on body sensors. To demonstrate wearable on-body sensing, a thin flexible CO2 gas sensor filament, which can be woven in fabric, is presented and evaluated. Wearable inductive resonant wireless power transfer (WPT) is achieved using textile embroidered coils on the bicycle’s handle and cycling gloves, achieving more than 80% WPT efficiency from the bicycle to the cyclist’s clothing, useful for powering mobile on-body sensors.
-A field modelling approach is presented exploiting approximate magnetic flux distributions. The method is an extension of flux-tubes and tubes-and-slices techniques. A combination of an equivalent magnetic circuit approach with improved description of relevant flux paths allows for a computationally efficient algorithm suitable for design optimization. The method is illustrated using an example of a linear switched reluctance motor and validated using finite element simulations.
An average rated torque estimation for generally saturable switched reluctance (SR) machines based on vector analysis is described. The proposed analytical method enables the switched reluctance machine designers to compute quickly and relatively accurately the rated torque of the machine. This approach offers simplicity, accuracy, and intuitive insight characteristic to analytical solutions of magnetically nonlinear problems otherwise achievable only with time-consuming computer-based numerical simulation tools. The suggested analytical methodology, therefore, offers immediate answers regarding the rated torque performance at the early stages of the machine sizing and design process. In this chapter, the switched reluctance machine rated torque calculation is derived based on the analytically estimated flux-linkage characteristic map and the knowledge of the DC bus voltage of the machine. It is further demonstrated that the proposed analytical rated torque calculations, based on vector analysis, enable construction of highly accurate instantaneous phase current profiles using a graphical method and thus aiding intuition and providing valuable insight into the nonlinear switched reluctance machine operation and control requirements. The proposed method will be found particularly suitable for those studying the nonlinear design and control of switched reluctance machine technologies for electric vehicle traction and industrial applications.
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