Saqib J Rind, Saba Javed, Yawar Rehman, Mohsin Jamil
{"title":"Sliding mode control rotor flux MRAS based speed sensorless induction motor traction drive control for electric vehicles","authors":"Saqib J Rind, Saba Javed, Yawar Rehman, Mohsin Jamil","doi":"10.3934/electreng.2023019","DOIUrl":null,"url":null,"abstract":"<abstract><p>Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R &amp; D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics.</p></abstract>","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AIMS Electronics and Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3934/electreng.2023019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics.
气候变化凸显了向更可持续的交通方式过渡的必要性。电动汽车(ev)和混合动力汽车(hev)为传统汽油动力汽车提供了一种很有前途的替代方案。然而,电力电子技术和先进控制系统的进步使得电动汽车和混合动力汽车的高性能牵引驱动器的实施变得容易。提出了一种新的滑模控制模型参考自适应系统(SMC-MRAS)速度估计器在牵引传动控制中的应用。然而,由于机器参数的不可预测的操作不确定性和未建模的非线性动力学,比例积分(PI)-MRAS可能不能产生令人满意的性能。该估计器消除了传统MRAS中使用的PI控制器。该方法利用两个回路产生转子磁链和电机转矩两种不同的误差信号。利用李亚普诺夫理论得到了SMC律的稳定性和动力学性质。提出的SMC-MRAS方法的潜力进行了模拟和实验验证,用于电动汽车的应用。开发了Matlab-Simulink环境,并将该方案应用于间接矢量控制。然而,为了进行实验验证,dSPACE 4011 R &采用D控制器板。此外,SMC-MRAS性能与PI-MRAS性能在调速性能、跟踪和估计误差以及误差信号的快速最小化方面有所不同。结果表明,该方案具有较强的速度估计能力、抗负载干扰能力和较快的误差动态特性。