Summary
The sensorless speed control of permanent magnet synchronous motor (PMSM) is gaining popularity in hybrid electric vehicle (HEV) applications leading to its enhanced safety, reliability, and ...cost savings. Speed control using vector control for PMSM‐fed HEV requires the speed encoder. When the speed sensor information fails, the inverter must ensure power delivery to the PMSM continuously by estimating the speed; this mode of operation is referred as limp‐home mode in HEV. In this paper, a speed sensorless scheme has been proposed for PMSM‐based HEV during limp‐home mode operation. This paper presents a model reference adaptive system (MRAS) speed estimator based on an adaptive neural network controller (NNC) for speed estimation of PMSM. In the HEV application, in case of speed/position encoder failure, the speed of the PMSM can be estimated by stator flux using stator current.The proposed method employs stator currents in the reference model to eliminate the DC drift problem. Furthermore, the NNC is employed in the adaptation mechanism to improve the Federal Test Procedure (FTP75) driving cycle performance. The performance of the proposed control scheme has been validated with dSPACE 1104 R & D rapid development controller using vector control for the PMSM during variable speed and torque, including the zero‐speed applications
This paper presents a model reference adaptive system (MRAS) speed observer based on an adaptive neural network controller for speed estimation of permanent magnet synchronous motor (PMSM) fed hybrid electric vehicle. When the speed sensor information fails, the inverter present in the power train of the vehicle must continuously ensure power delivery to the PMSM by estimating the speed; this mode of operation is referred to as limp‐home mode in EV. The proposed method employs stator currents in the reference model to eliminate the DC drift problem. The ANN‐based stator current MRAS speed observer has been tested for different driving cycles, such as FTP75, Indian urban, Indian highway drive cycle, and staircase reference input. The proposed control scheme enhances the system's dynamic performance by achieving better control and accuracy in speed regulation at different driving cycles, which is verified by comparing standard deviations (SDs) and relative error of speed response.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Summary
In this paper, a stator current‐based model reference adaptive system (SCMRAS) for indirect vector control of induction motor fed to hybrid electric vehicle (HEV) for improving the transient ...response during limp home period has been proposed. In the proposed SCMRAS, the measured stator currents are employed in voltage model to eliminate the integrator in reference model. The stator currents are estimated and are compared with actual current components to estimate the rotor speed. Further, to improve the performance of SCMRAS during limp home period, the PI controller in the adaptation mechanism is replaced with type 2 fuzzy logic controller (T2FLC). The prototype model of the proposed SCMRAS using dSPACE DS 1104 R&D controller board has been developed for implementing speed sensorless indirect vector control of induction motor drive. The performance of SCMRAS and proposed SCMRAS using T2FLC estimators during limp home period is compared.
To eliminate the integrator in the reference model, the proposed stator current model reference adaptive system uses the measured stator currents in the voltage model. The stator currents are estimated and compared to the actual current to estimate the rotor speed. The PI controller in the adaption mechanism is replaced with a type 2 fuzzy logic controller to improve Induction motor drive fed Hybrid electrical vehicle dynamic performance during the limp‐home period.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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