5 research outputs found

    High speed solid rotor permanent magnet machines: concept and design

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    This paper proposes a novel solid rotor topology for an Interior Permanent Magnet (IPM) machine, adopted in this case for an aircraft starter-generator design. The key challenge in the design is to satisfy two operating conditions which are: a high torque at start and a high speed at cruise. Conventional IPM topologies which are highly capable of extended field weakening are found to be limited at high speed due to structural constraints associated with the rotor material. To adopt the IPM concept for high speed operation, it is proposed to adopt a rotor constructed from semi-magnetic stainless steel, which has a higher yield strength than laminated silicon steel. To maintain minimal stress levels and also minimize the resultant eddy current losses due to the lack of laminations, different approaches are considered and studied. Finally, to achieve a better tradeoff between the structural and electromagnetic constraints, a novel slitted approach is implemented on the rotor. The proposed rotor topology is verified using electromagnetic, static structural and dynamic structural Finite Element (FE) analyses. An experiment is performed to confirm the feasibility of the proposed rotor. It is shown that the proposed solid rotor concept for an IPM fulfils the design requirements whilst satisfying the structural, thermal and magnetic limitations

    Extension of Virtual-Signal-Injection-Based MTPA Control for Interior Permanent-Magnet Synchronous Machine Drives Into the Field-Weakening Region

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    This paper presents a field-weakening control scheme to expand the speed operating region of the recently reported virtual signal injection control (VSIC) method for interior permanent-magnet synchronous machine (IPMSM) drives. Because of voltage saturation, the VSIC for IPMSM drives is not effective in the field-weakening region. A new control scheme is developed to guarantee that the torque can be controlled with minimum current amplitude. The proposed method realizes fast dynamic response and efficient operation of IPMSM drives in both constant torque and field-weakening regions by controlling the d-axis current through virtual signal injection and detection of the voltage saturation. The proposed method can track maximum torque per ampere (MTPA) points in the constant torque region and voltage-constrained MTPA points in the field-weakening region accurately without prior knowledge of accurate machine parameters. The proposed control method is demonstrated by both simulations and experiments under various operating conditions on a prototype IPMSM drive system

    Direct Torque Control for Silicon Carbide Motor Drives

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    Direct torque control (DTC) is an extensively used control method for motor drives due to its unique advantages, e.g., the fast dynamic response and the robustness against motor parameters variations, uncertainties, and external disturbances. Using higher switching frequency is generally required by DTC to reduce the torque ripples and decrease stator current total harmonic distortion (THD), which however can lower the drive efficiency. Through the use of the emerging silicon carbide (SiC) devices, which have lower switching losses compared to their silicon counterparts, it is feasible to achieve high efficiency and low torque ripple simultaneously for DTC drives. To overcome the above challenges, a SiC T-type neutral point clamped (NPC) inverter is studied in this work to significantly reduce the torque and flux ripples which also effectively reduce the stator current ripples, while retaining the fast-dynamic response as the conventional DTC. The unbalanced DC-link is an intrinsic issue of the T-type inverter, which may also lead to higher torque ripple. To address this issue, a novel DTC algorithm, which only utilizes the real voltage space vectors and the virtual space vectors (VSVs) that do not contribute to the neutral point current, is proposed to achieve inherent dc-link capacitor voltage balancing without using any DC-link voltage controls or additional DC-link capacitor voltages and/or neutral point current sensors. Both dynamic performance and efficiency are critical for the interior permanent-magnet (IPM) motor drives for transportation applications. It is critical to determine the optimal reference stator flux linkage to improve the efficiency further of DTC drives and maintain the stability of the drive system, which usually obtained by tuning offline and storing in a look-up table or calculated online using machine models and parameters. In this work, the relationship between the stator flux linkage and the magnitude of stator current is analyzed mathematically. Then, based on this relationship, a perturb and observe (P&O) method is proposed to determine the optimal flux for the motor which does not need any prior knowledge of the machine parameters and offline tuning. However, due to the fixed amplitude of the injected signal the P&O algorithm suffers from large oscillations at the steady state conditions. To mitigate the drawback of the P&O method, an adaptive high frequency signal injection based extremum seeking control (ESC) algorithm is proposed to determine the optimal reference flux in real-time, leading to a maximum torque per ampere (MTPA) like approach for DTC drives. The stability analysis and key parameters selection for the proposed ESC algorithm are studied. The proposed method can effectively reduce the motor copper loss and at the same time eliminate the time consuming offline tuning effort. Furthermore, since the ESC is a model-free approach, it is robust against motor parameters variations, which is desirable for IPM motors

    Real Time Torque Control of IPMM under Flux Variations

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ์„ค์Šน๊ธฐ.For IPMSM drives, accurate torque control and loss-minimizing operation are important issues to maintain the excellent features such as high efficiency and wide speed operating range. Thus, a current magnitude should be minimized while maintaining a torque accuracy under the base speed, i.e., in a maximum torque per ampere (MTPA) region. On the other hand, a current angle as well as the current magnitude should be adjusted above the base speed due to the limit of a dc-link voltage, i.e., in a flux-weakening region. For efficient operations, current references should be carefully decided by considering current and voltage constraints. However, it is difficult to find optimal current references due to the influence of magnetic saturation, cross-coupling effects and their variation according to operating conditions. In this thesis, an online minimum-copper-loss torque control is proposed to satisfy both a torque accuracy and high-efficiency operation in consideration of flux linkage variations due to magnetic saturation. Firstly, the minimum-copper-loss torque control can be dealt as a constrained optimization problem to satisfy both torque reference tracking and loss-minimizing operation. Nonlinear simultaneous equations can be derived from Lagrange multiplier method, which could be solved by numerical algorithms. Among them, Levenberg-Marquardt algorithm is employed as the numerical algorithm to guarantee a robust calculation of current references in a command optimizer. It can be optimized to alleviate calculation burden while maintaining the stability of the proposed algorithm. In addition, a torque reference limiter is implemented to satisfy the current and voltage constraints in real time. Stator flux linkages and dynamic inductances should be estimated to calculate the current references in the command optimizer. However, not only a dc offset but also low-order harmonics are contained in the estimated flux linkages due to various reasons. Thus, a distortion-minimizing flux observer is proposed to suppress the dc offset and harmonic flux errors by adopting frequency-adaptive observers. A proposed frequency-adaptive flux observer is appropriate to extract the fundamental flux linakges while keeping a simple structure even if a rotating speed of IPMSM varies. The fundamental flux linkages could be estimated quickly by the proposed current- and voltage- model based flux observer, which is implemented with not only the frequency-adaptive flux observer but also harmonic extractors. In addition, a dynamic inductance estimator is proposed to estimate the dynamic inductances by using a high-frequency signal injection method. The self- and mutual- inductance informations can be derived from a high-freqeuncy current response in case of a rotating vector voltage injection method. Thus, the voltage injection and corresponding signal processing method are derived based on the high-freqeuncy impedance modeling, where the effects of cross-coupling terms are included. As a result, the proposed estimator with sine-wave or square-wave injection method improves the precision of estimated dyanamic inductances in high speed operations. The feasibility of the proposed methods has been verified under various operating conditions by simulation and experimental results. Through the proposed command opimizer, the copper-loss minimizing torque control has been achieved under not only unsaturated but also highly-saturated operating conditions. In case of the tested IPMSM, the robust calculation of current references could be guaranteed even when the torque reference is over 80 % of a peak torque by applying the proposed Levenberg-Marquardt algorithm unlike the conventional Gauss-Newton algorithm. Furthermore, the proposed fundamental flux observer and dynamic inductance estimator have maintained high dynamic performance over a wide operating range. In case of the tested IPMSM, the fundamental stator flux linkages and dynamic inductances could be estimated even under 10 pu/s torque refenence variation and 3.75 pu/s load speed variation.๋†’์€ ํšจ์œจ๊ณผ ๋„“์€ ์šด์ „ ์˜์—ญ์„ ๊ฐ–๋Š” ๋งค์ž…ํ˜• ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ํšจ๊ณผ์ ์ธ ํ™œ์šฉ์„ ์œ„ํ•ด ์ •ํ™•ํ•œ ํ† ํฌ ์ œ์–ด์™€ ์†์‹ค ์ตœ์†Œํ™” ์šด์ „์€ ์ค‘์š”ํ•˜๊ฒŒ ๋‹ค๋ค„์ ธ ์™”๋‹ค. ๋”ฐ๋ผ์„œ, ๊ธฐ์ € ์†๋„ ์ดํ•˜์—์„œ๋Š” ์ถœ๋ ฅ ํ† ํฌ๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ ์ „๋ฅ˜ ํฌ๊ธฐ๋ฅผ ์ตœ์†Œํ™”ํ•˜๋„๋ก ํ•˜๋Š” ๋‹จ์œ„ ์ „๋ฅ˜ ๋‹น ์ตœ๋Œ€ ํ† ํฌ(MTPA) ์šด์ „ ๋ฐฉ์‹์ด ์‚ฌ์šฉ๋œ๋‹ค. ๋ฐ˜๋ฉด, ๊ธฐ์ € ์†๋„ ์ด์ƒ์—์„œ๋Š” ์ง๋ฅ˜๋‹จ ์ „์••์˜ ์ œํ•œ์œผ๋กœ ์ธํ•ด ์ „๋ฅ˜ ํฌ๊ธฐ์™€ ์ „๋ฅ˜ ๊ฐ์„ ๋ณ€๋™ํ•˜๋Š” ์•ฝ์ž์†(flux-weaking) ์šด์ „ ๋ฐฉ์‹์ด ์‚ฌ์šฉ๋œ๋‹ค. ํšจ์œจ์ ์ธ ์šด์ „์„ ์œ„ํ•ด ์ „๋ฅ˜ ์ง€๋ น์€ ์ „๋ฅ˜ ๋ฐ ์ „์•• ์ œํ•œ ์กฐ๊ฑด์„ ๊ณ ๋ คํ•˜์—ฌ ์ฃผ์˜ ๊นŠ๊ฒŒ ์„ค์ •๋˜์–ด์•ผ ํ•œ๋‹ค. ํ•˜์ง€๋งŒ, ์ „๋™๊ธฐ์˜ ์ž๊ธฐํฌํ™”, ๊ต์ฐจ๊ฒฐํ•ฉ ํ˜„์ƒ ๋ฐ ์šด์ „ ์กฐ๊ฑด ๋ณ€๋™์˜ ์˜ํ–ฅ์œผ๋กœ ์ธํ•ด ์ตœ์ ์˜ ์ „๋ฅ˜ ์ง€๋ น์„ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์€ ์‰ฝ์ง€ ์•Š๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ž๊ธฐํฌํ™”์— ์˜ํ•œ ์‡„๊ต์ž์† ๋ณ€๋™์„ ๊ณ ๋ คํ•˜์—ฌ ํ† ํฌ ์ •๋ฐ€๋„์™€ ๊ณ ํšจ์œจ ์šด์ „์„ ๋™์‹œ์— ๋งŒ์กฑํ•˜๋Š” ์‹ค์‹œ๊ฐ„ ์ตœ์†Œ ๋™์† ํ† ํฌ ์šด์ „์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์šฐ์„ ์ ์œผ๋กœ, ์ตœ์†Œ ๋™์† ํ† ํฌ ์šด์ „์€ ํ† ํฌ ์ง€๋ น ์ถ”์ข…๊ณผ ์†์‹ค ์ตœ์†Œํ™” ์šด์ „์„ ๋งŒ์กฑํ•˜๋Š” ์ œํ•œ ์ตœ์ ํ™” ๋ฌธ์ œ๋กœ ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Š” ๋ผ๊ทธ๋ž‘์ฆˆ ์Šน์ˆ˜๋ฒ•์„ ํ†ตํ•ด ๋น„์„ ํ˜• ์—ฐ๋ฆฝ ๋ฐฉ์ •์‹ ํ˜•ํƒœ๋กœ ์œ ๋„๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ˆ˜์น˜ ํ•ด์„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ†ตํ•ด ํ’€์–ด๋‚ผ ์ˆ˜ ์žˆ๋‹ค. ์—ฌ๋Ÿฌ ์ˆ˜์น˜ ํ•ด์„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๋น„๊ตํ•˜์—ฌ ์ „๋ฅ˜ ์ง€๋ น ๊ณ„์‚ฐ๊ธฐ์˜ ์•ˆ์ •์ ์ธ ๊ณ„์‚ฐ์„ ๋ณด์žฅํ•  ์ˆ˜ ์žˆ๋Š” ๋ ˆ๋ฒค๋ฒ„๊ทธ-๋งˆ์ฟผํŠธ๋ฒ•(Levenberg-Marquardt algorithm)์„ ์ˆ˜์น˜ ํ•ด์„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ์„ ํƒํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ œ์•ˆ๋œ ์ „๋ฅ˜ ์ง€๋ น ๊ณ„์‚ฐ๊ธฐ์˜ ์•ˆ์ •์„ฑ์„ ๋ณด์žฅํ•˜๋ฉด์„œ ๋™์‹œ์— ๊ณ„์‚ฐ ๋ถ€๋‹ด์„ ์ตœ์†Œํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ „๋ฅ˜ ๋ฐ ์ „์•• ์ œํ•œ์„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋ฐ˜์˜ํ•˜๊ธฐ ์œ„ํ•ด ํ† ํฌ ์ง€๋ น ์ œํ•œ๊ธฐ๋ฅผ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ์ „๋ฅ˜ ์ง€๋ น ๊ณ„์‚ฐ๊ธฐ์˜ ๋™์ž‘์„ ์œ„ํ•ด ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ์‡„๊ต์ž์† ๋ฐ ๋™์  ์ธ๋•ํ„ด์Šค์˜ ์ถ”์ •์ด ํ•„์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ถ”์ • ์‡„๊ต์ž์†์€ ๋‹ค์–‘ํ•œ ์ด์œ ๋กœ ์ธํ•ด DC ์˜คํ”„์…‹ ๋ฐ ์ €์ฐจ ๊ณ ์กฐํŒŒ๋ฅผ ํฌํ•จํ•˜๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ, ์ด๋Ÿฌํ•œ DC ์˜คํ”„์…‹ ๋ฐ ๊ณ ์กฐํŒŒ ์ž์† ์˜ค์ฐจ๋ฅผ ์–ต์ œํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ฃผํŒŒ์ˆ˜ ์ ์‘ ๊ด€์ธก๊ธฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์™ธ๋ž€ ์ตœ์†Œํ™” ์ž์† ๊ด€์ธก๊ธฐ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ์ฃผํŒŒ์ˆ˜ ์ ์‘ ์ž์† ๊ด€์ธก๊ธฐ๋Š” ํšŒ์ „ ์†๋„ ๋ณ€๋™ ์‹œ์—๋„ ๋‹จ์ˆœํ•œ ๊ตฌ์กฐ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ธฐ๋ณธํŒŒ ์‡„๊ต์ž์†๋งŒ์„ ์ถ”์ถœํ•ด ๋‚ผ ์ˆ˜ ์žˆ๋‹ค. ์ฃผํŒŒ์ˆ˜ ์ ์‘ ์ž์† ๊ด€์ธก๊ธฐ์™€ ๊ณ ์กฐํŒŒ ์ถ”์ถœ๊ธฐ๋กœ ๊ตฌํ˜„ํ•œ ์ œ์•ˆ๋œ ์ „๋ฅ˜ ๋ชจ๋ธ ๋ฐ ์ „์•• ๋ชจ๋ธ ์ž์† ๊ด€์ธก๊ธฐ๋ฅผ ํ†ตํ•ด ๊ธฐ๋ณธํŒŒ ์‡„๊ต์ž์†์„ ๋น ๋ฅด๊ฒŒ ์ถ”์ •ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋˜ํ•œ ๊ณ ์ฃผํŒŒ ์‹ ํ˜ธ ์ฃผ์ž…์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๋™์  ์ธ๋•ํ„ด์Šค๋ฅผ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•œ ๋™์  ์ธ๋•ํ„ด์Šค ์ถ”์ •๊ธฐ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ํšŒ์ „ ๋ฒกํ„ฐ ์ „์•• ์ฃผ์ž… ๋ฐฉ๋ฒ•์˜ ๊ฒฝ์šฐ, ์œ ๊ธฐ๋œ ๊ณ ์ฃผํŒŒ ์ „๋ฅ˜๋ฅผ ํ†ตํ•ด ์ž๊ธฐ ๋ฐ ์ƒํ˜ธ ์ธ๋•ํ„ด์Šค๋ฅผ ์ถ”์ถœํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ, ์ƒํ˜ธ ๊ต์ฐจํ•ญ์˜ ์˜ํ–ฅ์„ ํฌํ•จํ•œ ๊ณ ์ฃผํŒŒ ์ž„ํ”ผ๋˜์Šค ๋ชจ๋ธ๋ง์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ „์•• ์ฃผ์ž… ๋ฐ ์ „๋ฅ˜ ์‹ ํ˜ธ ์ฒ˜๋ฆฌ ๋ฐฉ์•ˆ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ์ •ํ˜„ํŒŒ ํ˜น์€ ๊ตฌํ˜•ํŒŒ ์ฃผ์ž…์— ๊ธฐ๋ฐ˜ํ•œ ์ œ์•ˆ๋œ ์ถ”์ •๊ธฐ๋ฅผ ํ†ตํ•ด ๊ณ ์† ์šด์ „ ์‹œ ๋™์  ์ธ๋•ํ„ด์Šค ์ถ”์ • ์ •๋ฐ€๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค์˜ ํƒ€๋‹น์„ฑ์€ ๋‹ค์–‘ํ•œ ์กฐ๊ฑด์—์„œ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆ๋˜์—ˆ๋‹ค. ์šฐ์„ ์ ์œผ๋กœ ์ œ์•ˆ๋œ ์ „๋ฅ˜ ์ง€๋ น ๊ณ„์‚ฐ๊ธฐ๋ฅผ ํ†ตํ•ด ์ž๊ธฐํฌํ™”๊ฐ€ ๊ฑฐ์˜ ์—†๋Š” ์˜์—ญ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํฌํ™”๊ฐ€ ์‹ฌํ•œ ์˜์—ญ์—์„œ๋„ ์‹ค์‹œ๊ฐ„ ์ตœ์†Œ ๋™์† ํ† ํฌ ์šด์ „์ด ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์‹œํ—˜์šฉ ์ „๋™๊ธฐ์˜ ๊ฒฝ์šฐ, ๊ธฐ์กด ๊ฐ€์šฐ์Šค-๋‰ดํŠผ๋ฒ•๊ณผ ๋‹ฌ๋ฆฌ ์ œ์•ˆ๋œ ๋ ˆ๋ฒค๋ฒ„๊ทธ-๋งˆ์ฟผํŠธ๋ฒ•์„ ํ†ตํ•ด 0.8 pu ์ด์ƒ์˜ ํ† ํฌ ์ง€๋ น์— ๋Œ€ํ•ด์„œ๋„ ์ตœ์†Œ ๋™์† ํ† ํฌ ์šด์ „์ด ๊ฐ€๋Šฅํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ œ์•ˆ๋œ ๊ธฐ๋ณธํŒŒ ์ž์† ๊ด€์ธก๊ธฐ ๋ฐ ๋™์  ์ธ๋•ํ„ด์Šค ์ถ”์ •๊ธฐ๊ฐ€ ๋„“์€ ์šด์ „ ์˜์—ญ์—์„œ ์ถฉ๋ถ„ํ•œ ๋™ํŠน์„ฑ์„ ๊ฐ€์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์‹œํ—˜์šฉ ์ „๋™๊ธฐ์˜ ๊ฒฝ์šฐ, 10 pu/s ํ† ํฌ ์ง€๋ น ๋ณ€๋™ ๋ฐ 3.75 pu/s ๋ถ€ํ•˜ ์†๋„ ๋ณ€๋™์—์„œ๋„ ๊ธฐ๋ณธํŒŒ ์ž์† ๋ฐ ๋™์  ์ธ๋•ํ„ด์Šค๋ฅผ ์ž˜ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.์ œ 1์žฅ ์„œ๋ก  ๏ผ‘ 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๏ผ‘ 1.2 ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๏ผ— 1.3 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ ๏ผ˜ ์ œ 2์žฅ ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ํŠน์„ฑ ๋ฐ ์šด์ „ ๋ฐฉ์•ˆ ๏ผ™ 2.1 ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ํŠน์„ฑ ๏ผ‘๏ผ 2.1.1 ์ž๊ธฐํฌํ™” ๋ฐ ๊ต์ฐจ๊ฒฐํ•ฉ ํ˜„์ƒ ๏ผ‘๏ผ 2.1.2 ๊ณต๊ฐ„ ๊ณ ์กฐํŒŒ ๏ผ‘๏ผ– 2.1.3 ์ œ์ •์ˆ˜ ๋ณ€๋™์— ๋”ฐ๋ฅธ ํ† ํฌ ์˜ํ–ฅ ๏ผ‘๏ผ™ 2.1.4 ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ๋ชจ๋ธ๋ง ๏ผ’๏ผ“ 2.2 ๊ธฐ์กด์˜ ์†์‹ค ์ตœ์†Œํ™” ์šด์ „ ๋ฐฉ์•ˆ ๏ผ’๏ผ• 2.2.1 ์„ญ๋™ ๊ธฐ๋ฐ˜ ํƒ์ƒ‰ ๋ฐฉ๋ฒ• ๏ผ’๏ผ– 2.2.2 ์ˆ˜ํ•™์  ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ• ๏ผ’๏ผ˜ 2.2.3 ๊ธฐ์กด ์šด์ „ ๋ฐฉ์•ˆ์˜ ํŠน์„ฑ ๋น„๊ต ๏ผ“๏ผ’ 2.3 ์ œ์•ˆ๋œ ์†์‹ค ์ตœ์†Œํ™” ์šด์ „ ๋ฐฉ์•ˆ ๏ผ“๏ผ” ์ œ 3์žฅ ์ตœ์†Œ ๋™์† ์ „๋ฅ˜ ์ง€๋ น ๊ณ„์‚ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๏ผ“๏ผ– 3.1 ์ตœ์†Œ ๋™์† ์šด์ „ ๋ฐฉ์ •์‹ ๏ผ“๏ผ— 3.1.1 MTPA ์˜์—ญ ๏ผ“๏ผ˜ 3.1.2 ์•ฝ์ž์† ์˜์—ญ ๏ผ”๏ผ 3.2 ์ œ์•ˆ๋œ ์ „๋ฅ˜ ์ง€๋ น ๊ณ„์‚ฐ๊ธฐ [49] ๏ผ”๏ผ’ 3.2.1 ์ˆ˜์น˜ ํ•ด์„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฒ€ํ†  ๏ผ”๏ผ’ 3.2.2 ๋ ˆ๋ฒค๋ฒ„๊ทธ-๋งˆ์ฟผํŠธ๋ฒ•์˜ ๊ฐ์‡ ๋น„ ์˜ํ–ฅ ๊ณ ์ฐฐ ๏ผ•๏ผ” 3.2.3 ๊ณ„์‚ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ตฌํ˜„ ๏ผ–๏ผ 3.3 ์ œ์•ˆ๋œ ํ† ํฌ ์ง€๋ น ์ œํ•œ๊ธฐ ๏ผ–๏ผ’ 3.3.1 ์ตœ๋Œ€ ํ† ํฌ ์šด์ „ ๋ฐฉ์ •์‹ ๏ผ–๏ผ“ 3.3.2 ๊ณ„์‚ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ตฌํ˜„ ๏ผ–๏ผ• 3.4 ์šด์ „ ์˜์—ญ ํŒ๋ณ„ ๋ฐ ์ ˆํ™˜ ๋ฐฉ์•ˆ ๏ผ–๏ผ˜ 3.5 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ์‹คํ—˜ ๊ฒฐ๊ณผ ๏ผ—๏ผ• 3.5.1 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ๏ผ—๏ผ– 3.5.2 ์‹คํ—˜ ๊ฒฐ๊ณผ ๏ผ˜๏ผ’ ์ œ 4์žฅ ์™ธ๋ž€ ์ตœ์†Œํ™” ๊ธฐ๋ณธํŒŒ ์ž์† ๊ด€์ธก๊ธฐ ๏ผ˜๏ผ˜ 4.1 ๊ธฐ์กด ์ž์† ๊ด€์ธก๊ธฐ์˜ ํŠน์„ฑ ๏ผ˜๏ผ™ 4.2 ์ œ์•ˆ๋œ ์ฃผํŒŒ์ˆ˜ ์ ์‘ ์ž์† ๊ด€์ธก๊ธฐ [68] ๏ผ™๏ผ“ 4.3 ์ œ์•ˆ๋œ ๊ธฐ๋ณธํŒŒ ์ž์† ๊ด€์ธก๊ธฐ ๏ผ™๏ผ™ 4.3.1 ์ „์•• ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ์ž์† ๊ด€์ธก๊ธฐ ๏ผ™๏ผ™ 4.3.2 ์ „๋ฅ˜ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ์ž์† ๊ด€์ธก๊ธฐ ๏ผ‘๏ผ๏ผ” 4.4 ์ž์† ๊ด€์ธก๊ธฐ ๊ตฌํ˜„ ์‹œ ๊ณ ๋ ค ์‚ฌํ•ญ ๏ผ‘๏ผ๏ผ— 4.4.1 ์ „๋™๊ธฐ ์ œ์ •์ˆ˜ ์˜ค์ฐจ ๋ฐ ์˜คํ”„์…‹ ์˜ํ–ฅ ๋ถ„์„ ๏ผ‘๏ผ๏ผ— 4.4.2 ์ž์† ๊ด€์ธก๊ธฐ ๊ฐ„ ์ ˆํ™˜ ๋ฐฉ์•ˆ ๏ผ‘๏ผ๏ผ˜ 4.4.3 ๋””์ง€ํ„ธ ์‹œ์ง€์—ฐ์„ ๊ณ ๋ คํ•œ ๋ณด์ƒ ๋ฐฉ์•ˆ ๏ผ‘๏ผ‘๏ผ 4.5 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ์‹คํ—˜ ๊ฒฐ๊ณผ ๏ผ‘๏ผ‘๏ผ’ 4.5.1 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ๏ผ‘๏ผ‘๏ผ“ 4.5.2 ์‹คํ—˜ ๊ฒฐ๊ณผ ๏ผ‘๏ผ’๏ผ‘ ์ œ 5์žฅ ์‹ ํ˜ธ ์ฃผ์ž… ๊ธฐ๋ฐ˜ ๋™์  ์ธ๋•ํ„ด์Šค ์ถ”์ •๊ธฐ ๏ผ‘๏ผ’๏ผ™ 5.1 ์‹ ํ˜ธ ์ฃผ์ž… ์•Œ๊ณ ๋ฆฌ์ฆ˜ ํŠน์„ฑ ๏ผ‘๏ผ“๏ผ 5.2 ๊ณ ์ฃผํŒŒ ์ž„ํ”ผ๋˜์Šค ๋ชจ๋ธ๋ง ๏ผ‘๏ผ“๏ผ• 5.3 ์ œ์•ˆ๋œ ๋™์  ์ธ๋•ํ„ด์Šค ์ถ”์ •๊ธฐ ๏ผ‘๏ผ“๏ผ— 5.3.1 ์ •ํ˜„ํŒŒ ์ „์•• ๊ณ ์ฃผํŒŒ ์ฃผ์ž… ๏ผ‘๏ผ“๏ผ— 5.3.2 ๊ตฌํ˜•ํŒŒ ์ „์•• ๊ณ ์ฃผํŒŒ ์ฃผ์ž… ๏ผ‘๏ผ”๏ผ‘ 5.4 ๊ณ ์ฃผํŒŒ ์‹ ํ˜ธ ์ฃผ์ž… ์‹œ ๊ณ ๋ ค ์‚ฌํ•ญ ๏ผ‘๏ผ”๏ผ– 5.4.1 ๊ณต๊ฐ„ ๊ณ ์กฐํŒŒ ์˜ํ–ฅ ๏ผ‘๏ผ”๏ผ– 5.4.2 ๋ถ€์ฐจ์ ์ธ ์†์‹ค ๋ฐ ์˜ค์ฐจ ์˜ํ–ฅ ๏ผ‘๏ผ”๏ผ— 5.5 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ์‹คํ—˜ ๊ฒฐ๊ณผ ๏ผ‘๏ผ”๏ผ™ 5.5.1 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ๏ผ‘๏ผ•๏ผ 5.5.2 ์‹คํ—˜ ๊ฒฐ๊ณผ ๏ผ‘๏ผ•๏ผ– ์ œ 6์žฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ์‹คํ—˜ ๏ผ‘๏ผ–๏ผ’ 6.1 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ๏ผ‘๏ผ–๏ผ“ 6.2 ์‹คํ—˜ ๊ฒฐ๊ณผ ๏ผ‘๏ผ–๏ผ— ์ œ 7์žฅ ๊ฒฐ๋ก  ๏ผ‘๏ผ—๏ผ’ 7.1 ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ๏ผ‘๏ผ—๏ผ’ 7.2 ํ–ฅํ›„ ๊ณผ์ œ ๏ผ‘๏ผ—๏ผ” ๋ถ€ ๋ก ๏ผ‘๏ผ—๏ผ— A. ์ œ์•ˆ๋œ ๊ธฐ๋ณธํŒŒ ์ž์† ๊ด€์ธก๊ธฐ์˜ ํƒ€ ์ „๋™๊ธฐ ์ ์šฉ ๊ฒฐ๊ณผ ๏ผ‘๏ผ—๏ผ— B. ์‹œํ—˜์šฉ ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ์‹คํ—˜ ์ œ์ •์ˆ˜ ๏ผ‘๏ผ™๏ผ‘ ์ฐธ๊ณ  ๋ฌธํ—Œ ๏ผ‘๏ผ™๏ผ” Abstract ๏ผ‘๏ผ™๏ผ™Docto

    EFFICIENCY OPTIMISED CONTROL OF INTERIOR MOUNTED PERMANENT MAGNET MACHINES FOR ELECTRIC VEHICLE TRACTION

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    Electric vehicles (EV) are playing a momentous role to the wide society as they facilitate the use of clean energy technologies. Interior mounted permanent magnet (IPM) synchronous machines are commonly employed in EVs owing to their superior characteristics such as high efficiency and power density and a wide field weakening operating range. High efficiency motor operation extends EVs drive range with the same amount of energy. Advanced control techniques to achieve high efficiency operation and smooth output torque production are, therefore, highly important areas to be researched. This thesis deals with the state-of-art motor drives and further develops advanced control strategies for minimum loss operation with good torque control quality. Modern AC drives can be classified in two groups, viz., field oriented control (FOC) and direct torque control (DTC). Whilst the former controls the phase currents for torque realization, the latter controls the torque directly. This thesis researches both and the novel advanced techniques are underpinned by extensive simulations and supported by experimental validations on a prototype motor designed for a specific class of EVs. The biggest challenge associated with the FOC drives is to improve the efficiency due to highly nonlinear characteristics of IPM machines. It has been discovered that even if the machine parameters are accurately modelled and stored in controllers to achieve optimal efficiency operation in a great number of FOC based IPM drives, there is still much deviation from the ideal operating points. A novel approach for online efficiency optimisation is proposed and comprehensively analysed in this thesis. The challenges pertinent to the DTC based IPM drives are to improve the observer quality and to reduce the strong coupling and the nonlinearity in the control loops. Novel observer structures, and the decoupled and linearized control techniques are among the novel contributions for DTC drives in this thesis. In addition, a comprehensive analysis of the relationship between stator flux vector and the torque has not been performed in the literature. The detailed analysis is made in this thesis and the maximum torque per voltage (MTPV) control theory for DTC drives is introduced. It is noteworthy that this thesis is based on comparative studies between the state-of-art and the proposed techniques throughout, and hence offers an insightful understanding for modern IPM drives
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