A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle

• Published in: Energies (Basel) Vol. 11; no. 9; p. 2322
• Main Authors: He, Changran, Wang, Guoye, Gong, Zhangpeng, Xing, Zhichao, Xu, Dongxin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2018
• Subjects: Braking systems; Control algorithms; Design optimization; Electric vehicles; multidisciplinary design optimization; optimum control; regenerative braking; regenerative–mechanical coupling
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11092322

Current regenerative braking systems in electric vehicles have several problems, such as complex structures, too many control parameters, and inconsistent braking responses. To solve these problems, a control algorithm with multidisciplinary design optimization (MDO) is proposed based on the novel regenerative–mechanical coupled brake-by-wire system. A dynamic model of the novel regenerative braking system was established to analyze the mechanism of coupled braking and propose a braking torque distribution strategy. To realize a better balance between the optimum braking stability and the maximum regenerative energy recovery based on the braking torque distribution strategy and sample points, the MDO mathematical model was developed to optimize the control parameters with the collaborative optimization algorithm. The finite sample points comprising the vehicle speed, battery state-of-charge, and braking severity were obtained through an optimal Latin hypercube design and represent the overall design space. A network was established based on the sample points and the optimization results. Using this network, the in-depth characteristics of the sample points and the optimization results were obtained through supervised learning to develop the control algorithm for vehicle braking. A simulation was performed using the normal braking condition, and the simulation results demonstrated that the control algorithm has higher control precision than conventional methods and better real-time performance than online optimization.