Parameter influence analysis in an optimized fuzzy stability control for a four-wheel independent-drive electric vehicle. (March 2022)

• Record Type: Journal Article
• Authors: Silva, Fabrício Leonardo; Silva, Ludmila C.A.; Eckert, Jony J.; Yamashita, Rodrigo Y.; Lourenço, Maria A.M.
• Is Part Of: Control engineering practice. Volume 120(2022)
• Publication Date: 2022-03
• Subjects: Electric vehicle -- Fuzzy logic control -- Drivetrain -- Multi-objective optimization -- Electronic differential; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.105000
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: This paper presents an optimized fuzzy logic control (FLC) applied to four-wheel independent-drive electric vehicles (FWID-EVs). The controller is developed to act as an Electronic Differential (ED), which changes the in-wheel motors torques to correct the vehicle trajectory during some specific maneuvers. The simulation determines the rotation, the lateral and longitudinal displacement of the vehicle, in which the nonlinear Magic Formula is applied to define the tires slip, associated with the load transfer during curves or drive/break situations and with the steering input. According to the literature, several parameters are used to provide stability controls of similar vehicles, for example, the sideslip angle and yaw rate. Therefore, a parameter influence analysis is carried out to find out the most relevant parameters to be monitored in the investigated FWID-EV. Once the relevant input parameters are defined, an optimum ED FLC is developed by means of a genetic algorithm multi-objective optimization with the objective of minimizing the trajectory error of the FWID-EV under a combination of the Sine with Dwell and constant steering standard maneuvers. The results show the best control configuration is a combination of steer and sideslip angles, with a reduced number of rules and less processing time (0.71 s). Besides, the proposed control presents a reduction in the trajectory error, improvement up to 95.1%, emphasizing that the parameter influence analysis allows finding an optimum, more efficient, and time-saving control solution. Therefore, a simple adjustment in the variables inputs can decrease the control process, without overshadowing the dynamic behavior. Graphical abstract: Highlights: Influence analysis of vehicle stability control input parameters. Multi-objective optimization procedure for a fuzzy vehicle stability control. Trajectory correction by means of electronic differential in-wheel EM control. Best control performance reached with only two input parameters.