Bilevel Quadratic Programming-Based Stability and Energy Saving Control for Electric Vehicles Using Neurodynamic Optimization
Kai Deng, Yuefeng Huang, Xiangyu Wang, Liang Li, Jian Song
Abstract
Stability and energy saving are essential issues for traditional and autonomous vehicles and can be ensured through optimization and control of steering, braking, and torque distribution in electric motors. While these three control mechanisms can be optimized separately using two-stage sequential optimization, the results are often suboptimal. As such, this study proposes simultaneous optimization and control of all three control units for the first time through bilevel quadratic programming (BQP) with neurodynamic optimization. First, a BQP technique is developed by assuming the lower optimization problem to be a feasible region for the upper optimization problem. Second, the upper and lower optimization problems are integrated into a QP task to achieve simultaneous optimization and control by removing separate steps required for two-stage sequential optimization. As the formulated QP problem is more generalized, including an irreversible positive semidefinite Hessian matrix and a switching subsystem, a novel neurodynamic optimization with a unique 2-norm energy function is established. It is then applied to solve the generalized BQP problem with high computational efficiency. Finally, hardware-in-the-loop experimental results confirmed the real-time effectiveness of the proposed control technique, which decreased the optimization time by 43.3%, and improved control performance while reducing the braking and motor torque required by 54.6% and 33.3%, respectively (compared with existing techniques).