Reliable EMF-Sensor-Fusion-Based Antilock Braking System for BLDC Motor In-Wheel Electric Vehicles

被引:8
作者
Dadashnialehi A. [1 ]
Bab-Hadiashar A. [1 ]
Cao Z. [2 ]
Hoseinnezhad R. [1 ]
机构
[1] School of Engineering, RMIT University, Melbourne, 3000, VIC
[2] School of Engineering, Swinburne University of Technology, Hawthorn, 3122, VIC
关键词
antilock braking system (ABS); brushless dc (BLDC) motor; data fusion; electric vehicle (EV); in-wheel technology; Sensor applications; wheel speed sensor;
D O I
10.1109/LSENS.2017.2705087
中图分类号
学科分类号
摘要
The design of current in-wheel electric vehicles (EVs) is predominantly based on using brushless dc (BLDC) motors as the propulsion system of the vehicle. The in-wheel design requires the juxtaposition of several powerful electromagnetic components that generate significant amount of electrical disturbances at each vehicle wheel and subsequently demands robustness to externally induced faults for its associated antilock braking system (ABS). In this paper, a sensor-fusion-based wheel speed measurement system is proposed for ABS in brushless in-wheel EVs. The objective is to exploit readily available back electromotive force (EMF) signals of the BLDC motor of an in-wheel hub as speed sensors (referred to as EMF-sensors in this paper) to improve the reliability of the associated ABS. The proposed methodology is based on fusion of wheel speed estimates acquired from the ABS sensor with their counterparts acquired from the EMF-sensors using a variety of fusion operators such as exponential class of ordered weighted averaging. The proposed design was extensively tested using actual ABS hardware and motors. Our experimental results showed that the wheel speed measurement accuracy of the proposed system is up to 70 higher compared to that of conventional vehicles. In addition, the proposed method is robust to ABS sensor failures such as a short-circuit fault. The method was also compared with brushed dc motor in-wheel EVs and experimental results showed significant improvements in accuracy and robustness. © 2017 IEEE.
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